Optimization of Critical Paths in Circuits with Level-Sensitive Latches

Size: px
Start display at page:

Download "Optimization of Critical Paths in Circuits with Level-Sensitive Latches"

Transcription

1 Optmzaton of Crtcal Paths n Crcuts wth Level-enstve Latches Tmothy M. Burks 1 and Karem A. akallah 2 1 ystems Technology and Archtecture Dvson, IBM Corporaton, Austn, TX 2 Department of Electrcal Engneerng and Computer cence, The Unversty of Mchgan, Ann Arbor, MI Abstract A smple extenson of the crtcal path method s presented whch allows more accurate optmzaton of crcuts wth level-senstve latches. The extended formulaton provdes a suffcent set of constrants to ensure that, when all slacks are non-negatve, the correspondng crcut wll be free of late sgnal tmng problems. Cycle stealng s drectly permtted by the formulaton. However, moderate restrctons may be necessary to ensure that the tmng constrant graph s acyclc. orcng the constrant graph to be acyclc allows a broad range of exstng optmzaton algorthms to be easly extended to better optmze crcuts wth level-senstve latches. We descrbe the extenson of two such algorthms, both of whch attempt to solve the problem of selectng parts from a lbrary to mnmze area subject to a cycle tme constrant. 1 The crtcal path method and tmng-drven desgn When a crcut must be desgned to satsfy strngent tmng constrants, we say that the desgn s tmng-drven. Researchers have descrbed a wde varety of tmngdrven desgn problems: logc synthess, retmng, transstor szng, part selecton, nput orderng, and placement and routng. Despte the range and varety of these problems, each approach s derved from a common framework for representng and enforcng tmng constrants: the Crtcal Path Method (CPM) [1]. The applcaton of CPM and a related technque called PERT to dgtal crcuts was frst descrbed by Krkpatrck and Clark [2] and later by Htchcock, mth, and Cheng [3]. In ths paper, crcuts are represented by a graph n whch drected arcs represent delays and nodes represent electrcally equpotental regons. Two specal nodes, the source and snk, group crcut nputs and outputs, respectvely. The crcut computaton tme s obtaned by makng a sngle pass through the graph. Begnnng wth the source node and proceedng n topologcal order, an event tme for each node s calculated: e ( v ) = max u P ( v ) [ e( u) + t uv, ] e( u) and e( v) are the event tmes for nodes u and v, t uv, s the delay of arc u v, and P( v) s the set of node predecessors of node v. When the event tme for the source node s zero, the event tme of the snk node gves the delay of the crcut. A crtcal path s a sequence of arcs that connects the source and snk nodes and whose delays determne the crcut completon tme. Crtcal paths can be dentfed by computng requred tmes n a sngle pass whch vsts nodes n reverse topologcal order. r ( v ) = mn u ( v ) [ r( u) t uv, ] r( u) and r( v) are the requred tmes for nodes u and v and ( v) s the set of successors of node v. The requred tme for the snk node s set to the tme that the crcut calculaton must complete. The slack of a node s defned as: s( v) = r( v) e( v) (3) A smlar quantty, float, s defned for each arc: f( u v) = r( v) e( u) t uv, (4) A crtcal path can be dentfed as a sequence of arcs havng the most negatve float n the graph. Orgnally, crtcal path methods were appled only to combnatonal crcuts. ynchronous sequental crcuts were analyzed by frst parttonng them nto combnatonal sectons whose nputs were drven from edge-trggered flp-flops or prmary nputs, and wth outputs connected to prmary outputs or edge-trggered flp-flops. When level-senstve latches were used, t was necessary to assume fxed sgnal departure tmes, effectvely treatng latches as edge-trggered devces. The purpose of ths paper s to relax these assumptons as much as possble. (1) (2) Permsson to copy wthout fee all or part of ths materal s granted, provded that the copes are not made or dstrbuted for drect commercal advantage, the ACM copyrght notce and the ttle of the publcaton and ts date appear, and notce s gven that copyng s by permsson of the Assocaton for Computng Machnery. To copy otherwse, or to republsh, requres a fee and/or specfc permsson ACM /94/0011/0468 $

2 2 CPM-based algorthms for tmng-drven part selecton The tmng-drven part selecton problem can be stated as follows: gven a netlst of parts and a lbrary contanng a dscrete set of mplementatons for each part, where each mplementaton has dfferent drve capablty, nput load, and cost (e.g. area or power), select an mplementaton for each part to mnmze the total cost subject to a fxed constrant on crcut tmng, typcally a mnmum cycle tme. A commonly-used delay model gves the delay through a part P as = τ + R C L τ s the ntrnsc delay of P, R s an effectve output resstance, and C L s the capactance seen by the part output. Typcally, τ and R decrease as the sze of P s ncreased and C L ncreases as the szes of the fanouts of P ncrease. Ths nonlnearty complcates the optmzaton problem, snce we cannot guarantee that the fastest crcut wll be the one composed of the largest parts. We examne two CPM-based part selecton algorthms, each of whch s easly extended to the optmzaton of crcuts wth level-senstve latches. The frst s an adaptaton of the TILO algorthm [5] for transstor szng. An teratve procedure, TILO frst dentfes the crtcal path or paths n a crcut and then selects one transstor from the path(s) to be reszed. The transstor chosen s the one wth the largest senstvty value, whch s defned as the amount of delay reducton per ncremental ncrease n area. Although later work showed that the TILO algorthm may not always produce optmal szngs [6], t s generally seen to produce good results wth moderate runnng tmes. TILO was orgnally developed to sze ndvdual transstors and allowed for a nearly-contnuous range of szes. However, a smlar approach can be used for the part selecton problem, and Ln et. al. developed a comparable procedure whch also used senstvty nformaton to gude szng [7]. In the verson of the algorthm that we use, each pass computes actual and requred tmes for each node. The arcs sharng the smallest float are examned, and from these, the gate wth the largest senstvty s reszed. Each teraton runs n tme lnearly related to the number of parts. The number of teratons vares wth the number of parts that must be reszed to satsfy the tmng constrants. The second algorthm was based on an algorthm for optmally szng a chan of parts developed by Hnsberger and Kolla, who also developed an algorthm for fanoutfree trees [8] and showed that the general problem of tm- (5) ng drven part selecton s NP-complete [8, 9]. To optmze arbtrary crcuts, Hnsberger and Kolla proposed usng ther szng algorthms to teratvely resze the most crtcal path or tree n a crcut. Iteraton stops when the target cycle tme s obtaned or when t s mpossble to reduce the delay of the crtcal path. Experments n [9] suggested that teratons of the path-based approach provded solutons of comparable qualty n sgnfcantly less tme than teratvely reszng trees. As a result, we use the smpler algorthm whch teratvely reszes chans of parts. 3 Extendng CPM for crcuts wth levelsenstve latches The approaches of the prevous secton were orgnally desgned to work on combnatonal logc only 1, but they can be easly extended to optmze across level-senstve latches, allowng latch arrval and departure tmes to move freely durng the optmzaton. We dstngush ths extended optmzaton as cross-latch optmzaton and consder t a superset of the nter-latch optmzaton technques orgnally developed usng CPM. Inter-latch optmzaton optmzes logc between latches, cross-latch optmzaton s able to optmze across latch boundares. To descrbe the necessary extensons, we use the latch tmng model developed by akallah, Mudge, and Olukotun [10]. Model equatons and constrants are lsted n Table 1, where we nclude only those relevant to the latest arrvng sgnals. Varables descrbng clock sgnals nclude the cycle tme T c, phase wdths T p, and endng tmes e p of each phase Φ p specfed n a common frame of reference. Crcut model parameters nclude latch setup tmes, and the maxmum delay between each connected par of latches 2 j. The data nput of each latch s modeled by the latest possble tme at whch a new sgnal can arrve A. Latch outputs are modeled by the latest tmes at whch new sgnals depart from the latch D. Arrval and departure tmes are defned n a frame-of-reference local to the correspondng latch. p denotes the clock controllng latch. A phase-shft operator Epj, p s used to convert sgnal tmes from the frame-of-reference of latch j to that of latch. In [11], t was observed that the constrants n Table 1 can be represented by a graph, whch was used to fnd the optmal clock schedule for a crcut. A smlar graph for- 1. TILO allowed optmzaton across a parameterzable number of latches but recommended that ths number be kept small, probably to avod dffcultes due to feedback loops n crcuts beng optmzed. 2. or smplcty, latch delays are omtted. They can be ncluded by the addton of terms to equaton (7) or (8). 469

3 Φ 1 Φ 2 T 1 e 1 T 2 e 2 0 T c 1 T c 2 T c B 3 T c 4 T c G B L 0 T c A G A A B C G B E 21, G C E 21, A 0 0 D 0 D G D A 1 0 D 1 D A E 3 12, 0 D 3 G A G D L 3 Z 1 C G C L 1 A 2 D 0 2 G E E E 0 E 12, 21, A 4 D 4 L 2 G E Φ 1 Example Crcut L 4 Φ2 Z 0 T c T 1 Constrant Graph T c T 2 gure 1: Late gnal Constrant Graph A T c (6) e( ) = r( ) = 0, e( ) > 0 f and only f a setup volaton D = max( A, T c T p ) (7) exsts n the crcut, and when postve, e( ) s the amount of the largest setup volaton n the crcut. A = max j I() ( D j + j E pj p) (8) If the crcut contans cycles of latches, we cannot drectly apply the CPM-based technques of ecton 2, E pj p = T (9) c ( ( e pj e p )modt c ) snce n cyclc crcuts of level-senstve latches, we must be careful not to volate constrants mposed by loops of Table 1: Tmng Model ummary transparent latches [11, 12]. Each such loop adds a constrant mulaton s shown n gure 1. In these late sgnal constrant of the form TOTAL nt c, where TOTAL s the graphs, each latch s represented by a par of nodes total delay around the loop and nt c s the tme avalable labeled A and D, whch correspond to the arrval and for sgnals to propagate around the loop. Although there s departure tmes for the latch. A zero-weght arc connects one constrant for each loop, they can be combned nto a the arrval and departure tme nodes and reflects the arrval sngle lower bound on T tme terms n equaton (7). Arcs labeled x model ndvdual c. Tmng-drven desgn requres slack values to ndcate gate delays. Arcs labeled E pj, p connect gates to whch specfc constrants are volated and how changes to latch nputs and complete the representaton of equaton ndvdual delays affect crcut tmng. The late sgnal constrant graphs were formulated so that slack and float values correspond to the amounts by whch tmes or delays (8). All paths through these E pj, p arcs must come from a latch controlled by phase p j, however, fanns of multple could be ncreased wthout volatng a setup constrant, but there s no smlar quantty avalable to ensure that the phases can be accommodated by duplcatng sectons of loop constrants are all satsfed. It s not dffcult to construct crcuts wth large setup tme slacks but wth a crt- the constrant graph. The clock system and ts assocated constrants are ncorporated nto the constrant graph wth cal loop constrant. or these crcuts, ncreases n delays two addtonal vertces and sets of assocated arcs. Clock based on setup slacks can result n tmng errors. dstrbuton s modeled by connectng a source vertex to There are a few alternatves for ensurng that loop constrants are satsfed. If the optmzaton s formulated as a each latch departure tme vertex wth arcs weghted T c T. These arcs model the occurrence of the rsng lnear or nonlnear programmng problem [13], the constrants edge of the clock controllng latch. Arrval tme constrants are enforced by connectng arrval tme vertces to wll be enforced mplctly. If we requre slack val- ues, one soluton would enumerate all possble cycles n a snk vertex wth arcs weghted T c. If necessary, the graph and calculate loop slacks based on total loop delays and loop tmng budgets. Clearly, however, ths s clock skew parameters can be ncluded n the weghts of mpractcal for general crcuts, as the number of loops can these arcs connected to the source and snk nodes. ettng grow exponentally wth the number of latches. 470

4 T c A 0 D T T c T c T T c T 0. unbroken subgraph 1. ALAP * T c A A D A D * * A = D * T c D 2. AAP 3. ACTUAL gure 2: Cycle-Breakng trateges Our approach breaks cycles n the constrant graph, modfyng the graph to guarantee that all loop constrants wll be satsfed. We artfcally break the cycles n the constrant graph by fxng the departure tmes of selected latches to some maxmum value and requrng that ther arrval tmes be no greater than these fxed departure tmes. Ths ensures that the departure tmes wll be no greater than the specfed values, allowng us to safely gnore the dependency between arrval and departure tmes for ths subset of latches. After breakng loops n ths manner, we have an acyclc graph to whch we can apply a wde varety of CPM-based analyss technques, ncludng those of ecton 2. The approach s conservatve, snce t only allows the optmzaton to cross a subset of latches, but s easy to mplement, and the remanng graph accurately models all the unmodfed latches n the crcut and allows the arrval and departure tmes at these latches to vary durng the optmzaton. We may fx the departure tmes at breakpont latches n several ways, ncludng:. 1. ALAP: gnals depart as late as possble. Departure tmes are set to ther latest possble values. 2. AAP: gnals depart as early as possble. Arrval tme constrants are tghtened to allow ths departure. 3. ACTUAL: gnals are assumed to arrve and depart * * at tmes A and D determned by a prelmnary tmng analyss. Each of the three cycle-breakng methods s llustrated n gure 2. ALAP and AAP arbtrarly fx a departure tme at ts latest or earlest possble value, possbly makng a feasble cycle tme appear nfeasble under the modfed constrants. The thrd method, ACTUAL, uses tmes deter- A D mned by a prelmnary analyss. If the analyss s performed at a feasble cycle tme, the added constrants wll not cause ths cycle tme to appear nfeasble. However, ths requres that all loop constrants be satsfed at the target cycle tme from the outset When selectng arcs to remove from the constrant graph, a reasonable goal would be to mnmze the effects of breakng loops on the tmng of the crcut beng optmzed. nce each broken arc adds extra tmng constrants, t would be natural to seek to break as few arcs as possble to make the crcut acyclc. Ths goal reduces to the problem EEDBACK ARC ET, a well-known NPhard problem [14]. However, a depth-frst traversal wll quckly fnd a suffcent set of arcs to remove that can make the graph acyclc. We recursvely traverse the graph, markng nodes as they are vsted. When a mark s found, a cycle has been located and can be observed n a stack of nodes currently beng expanded. We can then smply look back nto ths stack to fnd the frst A D arc, whch s removed to break the cycle, and contnue untl no more cycles reman. 4 Experments Inter-latch and cross-latch varatons of the optmzaton algorthms of ecton 2 were evaluated usng ICA89 benchmark crcuts. The orgnal ICA89 crcuts were synchronzed usng edge-trggered devces and a sngle-phase clock. To obtan a varety of level-senstve crcut structures, we transformed the benchmarks n three ways: 1. by replacng edge-trggered devces wth level-senstve latches and consderng the late sgnal constrants only (hold tme constrants are gnored). These crcuts have names begnnng wth the letter s, e.g., s by replacng edge-trggered devces wth pars of level-senstve latches controlled by alternate phases of a two-phase clock. The crcuts were then retmed to mnmze cycle tme usng a procedure smlar to that of Ish et al. [15]. These crcuts have names begnnng wth the letter t. 3. by usng a doublng transformaton descrbed by zymansk [11]. These crcuts have names begnnng wth the letter d. The crcuts used ranged n sze from 21 latches and 158 gates (s382) to 1642 latches and gates (d13207) Each was controlled by a symmetrc clock, and all twophase clocks were requred to be non-overlappng. nce the algorthms we consder nvolve modfyng crcut delays to satsfy a fxed clock schedule, ths restrcton s a convenence only and does not affect the generalty of the approach. 471

5 Parts were obtaned from the Texas Instruments 1-µ CMO standard cell lbrary [4]. Durng retmng transformatons, each part was assumed to be mplemented usng the smallest varant n the lbrary and snce exstng retmng algorthms do not allow for load-dependent delays, the retmed examples were obtaned by assumng that each gate drove a constant number of standard loads. All other analyses ncluded actual loadng effects along wth standard TI pre-layout estmators for nterconnect capactance. We sought to compare results obtaned usng nter-latch optmzaton wth those of cross-latch optmzatons usng the algorthms of ecton 2. Each algorthm was mplemented usng late sgnal constrant graphs. The same mplementatons performed nter-latch or cross-latch optmzaton, dependng on the presence of A D arcs n the graph. The two part selecton algorthms can be used to explore the relatonshp between the area and the mnmum cycle tme of a crcut. Each crcut s capable of operatng at a varety of speeds, dependng on the szng of ts component parts. Assumng all parts are at ther mnmum sze, we can compute a certan mnmum cycle tme for the crcut. In many cases t s possble to reduce ths mnmum by addng area to the crcut n the form of larger part varants. As a result, we expect an nverse relatonshp between area and mnmum cycle tme. gure 3-a shows the area vs. mnmum cycle tme relatonshp for benchmark t953 obtaned usng the TILO algorthm. The IMPLE curve represents smple nterlatch optmzaton. The ALAP and ACTUAL curves show results of cross-latch optmzatons breakng loops usng the respectve methods. or ACTUAL, ntal tmes were obtaned from a szng usng the IMPLE strategy to frst reduce latch-to-latch delays as much as possble. The arrval and departure tmes at breakponts were then computed at the mnmum cycle tme of the preszed crcut. gure 3-b shows the CPU seconds requred by each approach on a lghtly loaded DEC-staton 5000/120 (the ACTUAL curve does not nclude the constant addtonal tme requred for preszng). CPU tmes are drectly related to the amount of addtonal area requred; optmzatons requrng larger amounts of addtonal area requre a proportonately larger number of teratons of the TILO algorthm. Because of the addtonal flexblty allowed by tradng tme across latches, the area-delay curve for the ALAP and ACTUAL approaches are below and to the left of the IMPLE curve. Because less addtonal area s requred, the runnng tmes for these optmzatons are also less than those for the IMPLE strategy. Interestngly, the ACTUAL strategy was superor at small cycle tmes but was unable to fnd mnmal areas at large cycle tmes, probably because the optmzed startng pont ntroduced area cpu seconds b. CPU tme requred to reach target cycle tme gure 3: Optmzaton of t953 wth TILO algorthm an arrval tme constrant that could not be satsfed by the mnmum-area crcut. mlar curves were found for Hnsberger and Kolla s algorthm and are omtted due to space constrants. We observed Hnsberger and Kolla s algorthm to be slghtly faster, perhaps because t optmzes an entre path at a tme. The TILO algorthm found smaller mplementatons for a gven cycle tme, but the dfference was small. Both algorthms show smlar mprovements when crosslatch optmzatons are used, and both produce better results when ntalzed wth the tmng of a pre-szed crcut. Table 2 summarzes addtonal experments usng the TILO and Hnsberger-Kolla algorthms. Each benchmark crcut was optmzed wth a target cycle tme of T m, the mnmum cycle tme reachable usng nter-latch optmzaton. or the cross-latch optmzatons, loops were broken usng the ALAP method. In the table, the frst column dentfes benchmark crcuts and remanng columns lst ratos of addtonal area and optmzaton tme requred to reach the target cycle tmes for each crcut. In all cases, these ratos were less than one, ndcatng that the crosslatch optmzaton approaches requred less addtonal area and less CPU tme to reach the same cycle tme. 5 Conclusons "IMPLE" "ALAP" 1070 "ACTUAL" cycle tme a. Area vs. target cycle tme "IMPLE" "ALAP" "ACTUAL" cycle tme We see three general benefts of cross-latch optmzaton. rst, t s smple to mplement. Each algorthm we examned was formulated usng general CPM networks. 472

6 TILO Hnsberger-Kolla crcut A ALAP CPU ALAP A ALAP CPU ALAP A IMPLE CPU IMPLE A IMPLE CPU IMPLE s s s s s s s t t t t t t t d d d d d d N/A a d N/A averages Table 2: Experments conducted at T m a. unavalable ratos are due to runnng tmes outsde the measurable range. The extended formulaton ncorporates level-senstve latch tmng behavor and only requres the addtonal step of breakng cycles n the constrant graph. econd, cross-latch optmzaton produces better results. In all cases examned, the addtonal flexblty of cross-latch optmzaton found solutons of equal or better qualty to those of nter-latch optmzaton. Thrd, cross latch optmzaton adds no sgnfcant computatonal cost. The only extra computaton requred s the nexpensve loop-breakng step. In all cases examned, cross-latch optmzatons requred less tme than comparable nter-latch optmzatons. The runnng tmes of these algorthms depend on the dffculty of satsfyng tmng constrants; more accurately modelng latch tmng eases these constrants, allowng the algorthms to more quckly fnd a feasble soluton. To these we add the followng lmtaton: cross-latch optmzaton requres a target clock schedule. Tradtonal crtcal path methods mnmze cycle tme by maxmzng slack, ncreasng the margn on the setup constrants. Ths margn wll have a varyng nfluence on the cycle tme, dependng on the tme budgets of the related paths. Interlatch optmzaton does not necessarly requre a target clock. If the tme budgets of all paths are equal, then the mnmum cycle tme can be obtaned by maxmzng slack regardless of the cycle tme target. We beleve that many other CPM-based optmzatons can be smlarly extended to perform cross-latch optmzaton. Other areas for research nclude evaluaton of technques for loop breakng and development of gudelnes for ther use. Better optmzaton solutons may be found usng teratve approaches that modfy breakpont arrval and departure tmes durng optmzaton. Incremental tmng analyss would reduce runnng tmes. nally, snce the loop breakng modfcatons fundamentally restrct the soluton space, approaches whch can be drectly used on cyclc constrant graphs could allow further mprovement. References [1] K. Lockyer and J. Gordon, Crtcal Path Analyss and other Project Network Technques, Ptman, [2] T. I. Krkpatrck and N. R. Clark, PERT as an Ad to Logc Desgn, IBM Journal of Res. and Dev., vol. 10, no. 2, p , March [3] R. B. Htchcock, r., G. L. mth, and D. D. Cheng, Tmng Analyss of Computer Hardware, IBM Journal of Res. and Dev., vol. 26, no. 1, p , January [4] Texas Instruments, TC 700 eres 1-mcron CMO tandard Cells, R035B-D3857, [5] J. P. shburn and A. E. Dunlop, TILO: A Posynomal Programmng Approach to Transstor zng, n ICCAD- 85 Dgest of Techncal Papers, p , [6] J. M. hyu, A. angovann-vncentell, J. P. shburn, and A. E. Dunlop, Optmzaton-Based Transstor zng, IEEE Journal of old-tate Crcuts, 23(2), p , Aprl [7]. Ln, M. Marek-adowska, and E.. Kuh, Delay and Area Optmzaton n tandard-cell Desgn, n Proc. Desgn Automaton Conf., p , [8] U. Hnsberger and R. Kolla, A Cell-Based Approach to Performance Optmzaton of anout-ree Crcuts, IEEE Trans. on Computer-Aded Desgn, 11(10), p , October [9] U. Hnsberger and R. Kolla, Cell Based Performance Optmzaton of Combnatonal Crcuts, n Proc. European Conf. on Desgn Automaton, p , [10] K. A. akallah, T. N. Mudge, and O. A. Olukotun. checkt c and mnt c : Tmng Verfcaton and Optmal Clockng of ynchronous Dgtal Crcuts, n ICCAD-90 Dgest of Techncal Papers, p , [11] T. G. zymansk, Computng Optmal Clock chedules, In Proc. Desgn Automaton Conf., p , [12] T. M. Burks, K. A. akallah, and T. N. Mudge, Identfcaton of Crtcal Paths n Crcuts wth Level-enstve Latches, n ICCAD-92 Dgest of Techncal Papers, p , [13] W. Chuang,.. apatnekar, and I. N. Hajj, A Unfed Algorthm for Gate zng and Clock kew Optmzaton to Mnmze equental Crcut Area, n Proc. Desgn Automaton Conf., p , [14] R. M. Karp, Reducablty Among Combnatoral Problems, n R.E. Mller and J. W. Thatcher (eds.), Complexty of Computer Computatons, Plenum Press, New York, p , [15] A. Ish, C. E. Leserson, and M. C. Papaefthymou, Optmzng Two-Phase Level-Clocked Crcutry, n Advanced Research n VLI and Parallel ystems: Proceedngs of the 1992 Brown/MIT Conference, p ,

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

How Accurately Can We Model Timing In A Placement Engine?

How Accurately Can We Model Timing In A Placement Engine? How Accurately Can We Model Tmng In A Placement Engne? Amt Chowdhary, Karth Raagopal, Satsh Venatesan, Tung Cao, Vladmr Tourn, Yegna Parasuram, Bll Halpn Intel Corporaton Serra Desgn Automaton Synplcty,

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Explicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements

Explicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements Explct Formulas and Effcent Algorthm for Moment Computaton of Coupled RC Trees wth Lumped and Dstrbuted Elements Qngan Yu and Ernest S.Kuh Electroncs Research Lab. Unv. of Calforna at Berkeley Berkeley

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Improving The Test Quality for Scan-based BIST Using A General Test Application Scheme

Improving The Test Quality for Scan-based BIST Using A General Test Application Scheme _ Improvng The Test Qualty for can-based BIT Usng A General Test Applcaton cheme Huan-Chh Tsa Kwang-Tng Cheng udpta Bhawmk Department of ECE Bell Laboratores Unversty of Calforna Lucent Technologes anta

More information

EWA: Exact Wiring-Sizing Algorithm

EWA: Exact Wiring-Sizing Algorithm EWA: Exact Wrng-Szng Algorthm Rony Kay, Gennady Bucheuv and Lawrence T. Plegg Carnege Mellon Unversty Department of Electrcal and Computer Engneerng Pttsburgh, PA 15213 ABSTRACT The wre szng problem under

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

Repeater Insertion for Two-Terminal Nets in Three-Dimensional Integrated Circuits

Repeater Insertion for Two-Terminal Nets in Three-Dimensional Integrated Circuits Repeater Inserton for Two-Termnal Nets n Three-Dmensonal Integrated Crcuts Hu Xu, Vasls F. Pavlds, and Govann De Mchel LSI - EPFL, CH-5, Swtzerland, {hu.xu,vasleos.pavlds,govann.demchel}@epfl.ch Abstract.

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

Wishing you all a Total Quality New Year!

Wishing you all a Total Quality New Year! Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma

More information

An Entropy-Based Approach to Integrated Information Needs Assessment

An Entropy-Based Approach to Integrated Information Needs Assessment Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology

More information

Review of approximation techniques

Review of approximation techniques CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated

More information

Outline. Digital Systems. C.2: Gates, Truth Tables and Logic Equations. Truth Tables. Logic Gates 9/8/2011

Outline. Digital Systems. C.2: Gates, Truth Tables and Logic Equations. Truth Tables. Logic Gates 9/8/2011 9/8/2 2 Outlne Appendx C: The Bascs of Logc Desgn TDT4255 Computer Desgn Case Study: TDT4255 Communcaton Module Lecture 2 Magnus Jahre 3 4 Dgtal Systems C.2: Gates, Truth Tables and Logc Equatons All sgnals

More information

Meta-heuristics for Multidimensional Knapsack Problems

Meta-heuristics for Multidimensional Knapsack Problems 2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,

More information

Active Contours/Snakes

Active Contours/Snakes Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng

More information

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process

More information

A Min-Cost Flow Based Detailed Router for FPGAs

A Min-Cost Flow Based Detailed Router for FPGAs A Mn-Cost Flow Based Detaled Router for FPGAs eokn Lee Dept. of ECE The Unversty of Texas at Austn Austn, TX 78712 Yongseok Cheon Dept. of Computer cences The Unversty of Texas at Austn Austn, TX 78712

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Verification by testing

Verification by testing Real-Tme Systems Specfcaton Implementaton System models Executon-tme analyss Verfcaton Verfcaton by testng Dad? How do they know how much weght a brdge can handle? They drve bgger and bgger trucks over

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Reducing Frame Rate for Object Tracking

Reducing Frame Rate for Object Tracking Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

Modeling Multiple Input Switching of CMOS Gates in DSM Technology Using HDMR

Modeling Multiple Input Switching of CMOS Gates in DSM Technology Using HDMR 1 Modelng Multple Input Swtchng of CMOS Gates n DSM Technology Usng HDMR Jayashree Srdharan and Tom Chen Dept. of Electrcal and Computer Engneerng Colorado State Unversty, Fort Collns, CO, 8523, USA (jaya@engr.colostate.edu,

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES

PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES Ruxandra Olmd Faculty of Mathematcs and Computer Scence, Unversty of Bucharest Emal: ruxandra.olmd@fm.unbuc.ro Abstract Vsual secret sharng schemes

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

Topology Design using LS-TaSC Version 2 and LS-DYNA

Topology Design using LS-TaSC Version 2 and LS-DYNA Topology Desgn usng LS-TaSC Verson 2 and LS-DYNA Wllem Roux Lvermore Software Technology Corporaton, Lvermore, CA, USA Abstract Ths paper gves an overvew of LS-TaSC verson 2, a topology optmzaton tool

More information

LS-TaSC Version 2.1. Willem Roux Livermore Software Technology Corporation, Livermore, CA, USA. Abstract

LS-TaSC Version 2.1. Willem Roux Livermore Software Technology Corporation, Livermore, CA, USA. Abstract 12 th Internatonal LS-DYNA Users Conference Optmzaton(1) LS-TaSC Verson 2.1 Wllem Roux Lvermore Software Technology Corporaton, Lvermore, CA, USA Abstract Ths paper gves an overvew of LS-TaSC verson 2.1,

More information

Post-Layout Timing-Driven Cell Placement Using an Accurate Net Length Model with Movable Steiner Points *

Post-Layout Timing-Driven Cell Placement Using an Accurate Net Length Model with Movable Steiner Points * Post-Layout mng-drven Cell Placement Usng an Accurate Net Length Model wth Movable Stener Ponts * Amr H. Ajam and Massoud Pedram Department of Electrcal Engneerng - Systems Unversty of Southern Calforna

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

Parallel Branch and Bound Algorithm - A comparison between serial, OpenMP and MPI implementations

Parallel Branch and Bound Algorithm - A comparison between serial, OpenMP and MPI implementations Journal of Physcs: Conference Seres Parallel Branch and Bound Algorthm - A comparson between seral, OpenMP and MPI mplementatons To cte ths artcle: Luco Barreto and Mchael Bauer 2010 J. Phys.: Conf. Ser.

More information

CPE 628 Chapter 2 Design for Testability. Dr. Rhonda Kay Gaede UAH. UAH Chapter Introduction

CPE 628 Chapter 2 Design for Testability. Dr. Rhonda Kay Gaede UAH. UAH Chapter Introduction Chapter 2 Desgn for Testablty Dr Rhonda Kay Gaede UAH 2 Introducton Dffcultes n and the states of sequental crcuts led to provdng drect access for storage elements, whereby selected storage elements are

More information

Load-Balanced Anycast Routing

Load-Balanced Anycast Routing Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

Simulation Based Analysis of FAST TCP using OMNET++

Simulation Based Analysis of FAST TCP using OMNET++ Smulaton Based Analyss of FAST TCP usng OMNET++ Umar ul Hassan 04030038@lums.edu.pk Md Term Report CS678 Topcs n Internet Research Sprng, 2006 Introducton Internet traffc s doublng roughly every 3 months

More information

Dynamic Voltage Scaling of Supply and Body Bias Exploiting Software Runtime Distribution

Dynamic Voltage Scaling of Supply and Body Bias Exploiting Software Runtime Distribution Dynamc Voltage Scalng of Supply and Body Bas Explotng Software Runtme Dstrbuton Sungpack Hong EE Department Stanford Unversty Sungjoo Yoo, Byeong Bn, Kyu-Myung Cho, Soo-Kwan Eo Samsung Electroncs Taehwan

More information

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION 24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and

More information

A Facet Generation Procedure. for solving 0/1 integer programs

A Facet Generation Procedure. for solving 0/1 integer programs A Facet Generaton Procedure for solvng 0/ nteger programs by Gyana R. Parja IBM Corporaton, Poughkeepse, NY 260 Radu Gaddov Emery Worldwde Arlnes, Vandala, Oho 45377 and Wlbert E. Wlhelm Teas A&M Unversty,

More information

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems A Unfed Framework for Semantcs and Feature Based Relevance Feedback n Image Retreval Systems Ye Lu *, Chunhu Hu 2, Xngquan Zhu 3*, HongJang Zhang 2, Qang Yang * School of Computng Scence Smon Fraser Unversty

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit LOOP ANALYSS The second systematic technique to determine all currents and voltages in a circuit T S DUAL TO NODE ANALYSS - T FRST DETERMNES ALL CURRENTS N A CRCUT AND THEN T USES OHM S LAW TO COMPUTE

More information

Modeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach

Modeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach Modelng, Manpulatng, and Vsualzng Contnuous Volumetrc Data: A Novel Splne-based Approach Jng Hua Center for Vsual Computng, Department of Computer Scence SUNY at Stony Brook Talk Outlne Introducton and

More information

Routability Driven Modification Method of Monotonic Via Assignment for 2-layer Ball Grid Array Packages

Routability Driven Modification Method of Monotonic Via Assignment for 2-layer Ball Grid Array Packages Routablty Drven Modfcaton Method of Monotonc Va Assgnment for 2-layer Ball Grd Array Pacages Yoch Tomoa Atsush Taahash Department of Communcatons and Integrated Systems, Toyo Insttute of Technology 2 12

More information

Constructing Minimum Connected Dominating Set: Algorithmic approach

Constructing Minimum Connected Dominating Set: Algorithmic approach Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected

More information

Greedy Technique - Definition

Greedy Technique - Definition Greedy Technque Greedy Technque - Defnton The greedy method s a general algorthm desgn paradgm, bult on the follong elements: confguratons: dfferent choces, collectons, or values to fnd objectve functon:

More information

CHAPTER 4 PARALLEL PREFIX ADDER

CHAPTER 4 PARALLEL PREFIX ADDER 93 CHAPTER 4 PARALLEL PREFIX ADDER 4.1 INTRODUCTION VLSI Integer adders fnd applcatons n Arthmetc and Logc Unts (ALUs), mcroprocessors and memory addressng unts. Speed of the adder often decdes the mnmum

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

Classification / Regression Support Vector Machines

Classification / Regression Support Vector Machines Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

More information

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

More information

Intra-Parametric Analysis of a Fuzzy MOLP

Intra-Parametric Analysis of a Fuzzy MOLP Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral

More information

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place

More information

3. CR parameters and Multi-Objective Fitness Function

3. CR parameters and Multi-Objective Fitness Function 3 CR parameters and Mult-objectve Ftness Functon 41 3. CR parameters and Mult-Objectve Ftness Functon 3.1. Introducton Cogntve rados dynamcally confgure the wreless communcaton system, whch takes beneft

More information

MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method

MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method MIED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part : the optmzaton method Joun Lampnen Unversty of Vaasa Department of Informaton Technology and Producton Economcs P. O. Box 700

More information

Virtual Machine Migration based on Trust Measurement of Computer Node

Virtual Machine Migration based on Trust Measurement of Computer Node Appled Mechancs and Materals Onlne: 2014-04-04 ISSN: 1662-7482, Vols. 536-537, pp 678-682 do:10.4028/www.scentfc.net/amm.536-537.678 2014 Trans Tech Publcatons, Swtzerland Vrtual Machne Mgraton based on

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

Real-Time Systems. Real-Time Systems. Verification by testing. Verification by testing

Real-Time Systems. Real-Time Systems. Verification by testing. Verification by testing EDA222/DIT161 Real-Tme Systems, Chalmers/GU, 2014/2015 Lecture #8 Real-Tme Systems Real-Tme Systems Lecture #8 Specfcaton Professor Jan Jonsson Implementaton System models Executon-tme analyss Department

More information

Channel 0. Channel 1 Channel 2. Channel 3 Channel 4. Channel 5 Channel 6 Channel 7

Channel 0. Channel 1 Channel 2. Channel 3 Channel 4. Channel 5 Channel 6 Channel 7 Optmzed Regonal Cachng for On-Demand Data Delvery Derek L. Eager Mchael C. Ferrs Mary K. Vernon Unversty of Saskatchewan Unversty of Wsconsn Madson Saskatoon, SK Canada S7N 5A9 Madson, WI 5376 eager@cs.usask.ca

More information

Scheduling with Integer Time Budgeting for Low-Power Optimization

Scheduling with Integer Time Budgeting for Low-Power Optimization Schedlng wth Integer Tme Bdgetng for Low-Power Optmzaton We Jang, Zhr Zhang, Modrag Potkonjak and Jason Cong Compter Scence Department Unversty of Calforna, Los Angeles Spported by NSF, SRC. Otlne Introdcton

More information

An Image Fusion Approach Based on Segmentation Region

An Image Fusion Approach Based on Segmentation Region Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua

More information

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr) Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute

More information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

An efficient iterative source routing algorithm

An efficient iterative source routing algorithm An effcent teratve source routng algorthm Gang Cheng Ye Tan Nrwan Ansar Advanced Networng Lab Department of Electrcal Computer Engneerng New Jersey Insttute of Technology Newar NJ 7 {gc yt Ansar}@ntedu

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

Test-Cost Modeling and Optimal Test-Flow Selection of 3D-Stacked ICs

Test-Cost Modeling and Optimal Test-Flow Selection of 3D-Stacked ICs Test-Cost Modelng and Optmal Test-Flow Selecton of 3D-Stacked ICs Mukesh Agrawal, Student Member, IEEE, and Krshnendu Chakrabarty, Fellow, IEEE Abstract Three-dmensonal (3D) ntegraton s an attractve technology

More information

CHAPTER 3 SEQUENTIAL MINIMAL OPTIMIZATION TRAINED SUPPORT VECTOR CLASSIFIER FOR CANCER PREDICTION

CHAPTER 3 SEQUENTIAL MINIMAL OPTIMIZATION TRAINED SUPPORT VECTOR CLASSIFIER FOR CANCER PREDICTION 48 CHAPTER 3 SEQUENTIAL MINIMAL OPTIMIZATION TRAINED SUPPORT VECTOR CLASSIFIER FOR CANCER PREDICTION 3.1 INTRODUCTION The raw mcroarray data s bascally an mage wth dfferent colors ndcatng hybrdzaton (Xue

More information

Video Proxy System for a Large-scale VOD System (DINA)

Video Proxy System for a Large-scale VOD System (DINA) Vdeo Proxy System for a Large-scale VOD System (DINA) KWUN-CHUNG CHAN #, KWOK-WAI CHEUNG *# #Department of Informaton Engneerng *Centre of Innovaton and Technology The Chnese Unversty of Hong Kong SHATIN,

More information

Configuration Management in Multi-Context Reconfigurable Systems for Simultaneous Performance and Power Optimizations*

Configuration Management in Multi-Context Reconfigurable Systems for Simultaneous Performance and Power Optimizations* Confguraton Management n Mult-Context Reconfgurable Systems for Smultaneous Performance and Power Optmzatons* Rafael Maestre, Mlagros Fernandez Departamento de Arqutectura de Computadores y Automátca Unversdad

More information

Space-Optimal, Wait-Free Real-Time Synchronization

Space-Optimal, Wait-Free Real-Time Synchronization 1 Space-Optmal, Wat-Free Real-Tme Synchronzaton Hyeonjoong Cho, Bnoy Ravndran ECE Dept., Vrgna Tech Blacksburg, VA 24061, USA {hjcho,bnoy}@vt.edu E. Douglas Jensen The MITRE Corporaton Bedford, MA 01730,

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT 3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Optimizing Document Scoring for Query Retrieval

Optimizing Document Scoring for Query Retrieval Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng

More information

Synthesizer 1.0. User s Guide. A Varying Coefficient Meta. nalytic Tool. Z. Krizan Employing Microsoft Excel 2007

Synthesizer 1.0. User s Guide. A Varying Coefficient Meta. nalytic Tool. Z. Krizan Employing Microsoft Excel 2007 Syntheszer 1.0 A Varyng Coeffcent Meta Meta-Analytc nalytc Tool Employng Mcrosoft Excel 007.38.17.5 User s Gude Z. Krzan 009 Table of Contents 1. Introducton and Acknowledgments 3. Operatonal Functons

More information