Breakpoints and Breakpoint Detection in Source Level Emulation

Size: px
Start display at page:

Download "Breakpoints and Breakpoint Detection in Source Level Emulation"

Transcription

1 Breakpoits ad Breakpoit Detectio i Source Level Emulatio Gerot Koch 1, Udo Kebschull 1, Wolfgag Rosestiel 2 1 Forschugszetrum Iformatik (FZI), Haid-ud-Neu-Straße 10-14, D Karlsruhe, Germay 2 FZI ad Uiversity of Tübige, Sad 13, D Tübige, Germay Abstract I this paper we discuss, what breakpoits i Source Level Emulatio a are, how we ca work with them ad how we have to chage the cicuit geerated by high level sythesis to do so. We show the details of breakpoit ecodig ad detectio i our approach. The preseted approach allows for breakpoit detectio by hardware meas without seriously slowig dow the circuit or dramatically icreasig its size. 1. Itroductio For years, high level sythesis is of icreasig importace i desig automatio. The icreasig umber of commercial available tools for high level sythesis idicates that the abstractio level of the desig etry will raise to the algorithmic level at least for some kids of applicatios While there is much effort spet i methods for sythesis, there are oly few groups cocetratig o desig validatio ad debuggig of high level specificatios. I geeral, validatio ca be doe by simulatio ad emulatio. Simulatio is a powerful meas to detect mistakes i the specificatio. Some groups work o simulatio tools for whole systems cosistig of hardware ad software ad eve other techologies like mechaics [1][2]. However, simulatio ca oly be applied to the specificatio level, ad eve there, a desig ca oly be partially validated because simulatio is very time cosumig. Validatig at the specificatio level is ot sufficiet, if high level sythesis is applied. There are iterface compoets ad custom compoets i such desigs, which are ot visible at the algorithmic level. So validatio is also ecessary o lower levels of abstractio. But there, simulatio is less applicable because it is too slow. a. Work partially supported by the Deutsche Forschugsgemeischaft (DFG) Emulatio is a techique, which allows for validatio of desigs at a low abstractio level almost i real time [3]. But emulatio works at the gate level. Thus, a desiger caot relate the probed sigals to the specificatio. For this reaso, debuggig is oly possible at the I/O sigals of the system. Quicktur has addressed this problem with the HDL-ICE [4] system, which allows to relate the probed sigals to a RT-specificatio, but still there is a big gap betwee the algorithmic level ad the level, where validatio is doe. I [10], we have proposed Source Level Emulatio (SLE) as a method to close this gap by combiig behavioral simulatio with hardware emulatio. The idea of SLE is to ru the applicatio o a emulator hardware ad to keep the correlatio betwee hardware elemets ad the behavioral VHDL source such that it is possible to stop the hardware by iterruptig the clock ad to extract values of variables i the source code by readig registers of the circuit. This correlatio is maily obtaied through loggig the sythesis steps of the high level sythesis. SLE allows for symbolic debuggig of a ruig hardware similar to software debuggig. This icludes the examiatio of variables, the settig of breakpoits, ad sigle step operatio. All this is possible with the applicatio ruig as a real hardware implemetatio o a hardware emulator. By backaotatig the values read from the circuit, we ca do debuggig at the source code level. We do ot eed to capture the eviromet of the applicatio i a simulator. We just coect the emulator to the eviromet of the applicatio. There is o eed to write (ofte eormous) simulatio eviromets i VHDL which is eve better sice these simulatio eviromets are at least as fault-proe as the applicatio itself. I this paper, we discuss two importat issues of SLE i more detail: The hardware that we itroduce additioally ito the geerated circuit to set ad detect breakpoits, to read data path registers, ad to cotrol the circuit operatio. The secod issue of this paper is to defie, what a breakpoit is i terms of hardware ad how we ca ma-

2 age to set ad detect such breakpoits without eedig a tremedous hardware overhead ad without extremely slowig dow the circuit. I the ext chapter we discuss the breakpoit issue. I chapter 3 we give a detailed descriptio of the hardware extesios we eed for SLE. We coclude the paper with some results i chapter 4 ad a summary. Y := A + B; A B i++ X := A(i) * B(i); Y := Y + X; A(i) B(i) 2. Breakpoits i SLE 2.1. Breakpoit defiitio High level sythesis geerates a circuit from a software program like specificatio. The circuit cotais a data path, where the data computatios are carried out, ad a cotroller, which cotrols whe a compoet i the data path is active. The cotroller is a fiite state machie that sets cotrol values for the data path ad reacts to coditio values from the data path. Sice we wat to debug a ruig circuit at the source code level, we have to defie a breakpoit as somethig which is visible i the source code. O the other had, a breakpoit must also be visible i the circuit to eable us to detect it. Thus, we defie a breakpoit as a operatio like +, *, etc. If such a operatio is executed by a compoet, we ca detect it i the ruig hardware. Defiitio 1. A breakpoit is a triple (Op, IO, T). It is iterpreted as the time T, at which the iput or output IO of operatio Op trasports data. Defiitio 2. A breakpoit is called detectable breakpoit, if Op is implemeted by a hardware compoet ad if IO is visible i the data path at the RT level. Figure 1 illustrates the defiitios. Part a) shows all iputs ad outputs of a '+' operatio as visible i the data path. Part b) shows a example, where the iput/output X is ot visible due to a optimizatio durig sythesis. I the followig, we will use breakpoit as syoym to detectable breakpoit. The use of o detectable breakpoits is ot allowed sice we caot detect them by hardware meas Breakpoit types Oe possibility to detect the time T of a breakpoit i the hardware is by lookig at the cotroller states, sice the cotroller maages the sequetial behavior of the data path. The relatio betwee the breakpoit time, ad a cotroller state is static ad kow from the sythesis process. I the data path, there are operatios which are always executed i a certai cotroller state. We call breakpoits ADD Y Figure 1. a) A, B, Y as detectable breakpoits ad b) X as o detectable bp at this type of operatios Moore breakpoits. Such a breakpoit is reached, if the cotroller state matches the breakpoit time T. There are also operatios, that are executed at trasitios of cotroller states. To detect breakpoits at those operatios, we eed to kow about the curret state ad the ext state of the cotroller. These breakpoits are called Mealy breakpoits. A Mealy breakpoit is reached, if the cotroller state matches the breakpoit time T ad if the ext state is right oe for the breakpoit. Both, Mealy ad Moore breakpoits, ca be exteded by data depedecy. This allows for settig a coditioal breakpoit. For istace we could say "stop at B i the expressio Y := A * B; if B = 5." More sophisticated coditios are possible, but require also more hardware overhead. Naturally, we could always stop at a breakpoit ad do the evaluatio of the coditioal expressio i software after readig the correspodig values from the circuit. This is ot what we wat, sice it would make it impossible to ru the circuit i its real eviromet. Thus we have to keep the coditios very simple Breakpoit ecodig Mult_Acc a) b) Sice we wat to react, if a breakpoit is reached by iterruptig the circuit, we have to detect the breakpoits by hardware meas. To do that, we eed to ecode the breakpoits i a way that allows for very simple testig whether a cotroller state correspods to a breakpoit or ot. This ecodig of the breakpoit (breakpoit ID) has to satisfy the followig costraits: Differet breakpoits must have differet IDs. For each cotroller state that is elemet of a particular breakpoit set the cotroller must produce the correspodig breakpoit ID. This ca be compared to a breakpoit register. If equal, the breakpoit is reached. Above, we have implicitly metioed. that a breakpoit would correspod to oe cotroller state. The the state Y

3 ecodig would also serve as breakpoit ecodig for hardware breakpoit detectio. The reality is ot as simple. A breakpoit ca be represeted by a whole set of cotroller states. Furthermore, these sets of states ca itersect, as the example i figure 2 shows. For istace this could be caused by path based schedulig [7]. I Figure 2 we have costructed a case to show what if cod the C := M + N; (OP1) else X := X1 + X2; (OP2) ed if; Y1 := A + B; (OP3) Y2 := B + C; (OP4) Y3 := Y1 + A; (OP5) Y4 := Y1 + B; (OP6) Y5 := Y2 + Y3; (OP7) OP1,OP3 OP4,OP5 OP6,OP7 cod OP3,OP4 OP5,OP6 OP7,OP2 cod OP1 OP3 OP4 OP5 OP6 OP7 OP2 Figure 2. Breakpoits represeted by a itersectig set of cotroller states ca happe. For simplicity, we idetify the breakpoit triple (Op, IO, T) by the operatio Op.The code fragmet does ot compute aythig sesible, but a similar case may occur i a real example. If we costrai the hardware resources to two adders ad a comparator, schedulig ad cotroller costructio ca result i a cotroller as show. The left path of the cotroller shows the if-path, the right oe shows the else-path. There are 8 detectable breakpoits, three of them represeted by oe cotroller state, the others represeted by two states each. I state 3, for istace, we have to produce the ID of breakpoit OP3 ad of breakpoit OP4, but we ca oly have oe cotroller output word i this state. A easy but iefficiet way to solve the problem would be to code each breakpoit with three bit ad to have a 6-bit word as cotroller output cotaiig both IDs. The first 3 bit would the idicate OP3, while the last 3 bit would idicate OP4. To detect breakpoit OP3, we would tell the breakpoit comparator to igore the last 3 bit. States 3 ad 4 have to show the same patter i the first 3 bit the. The same rule ca be applied to each state. The we would have to add a 6 bit word to the cotroller outputs just for breakpoit detectio. This ca cause a explosio of the cotroller logic i larger applicatios. I geeral, this ca be formulated as the problem to biary ecode elemets of a arbitrary set structure i a way, that, give a particular subset code, oe ca see whether or ot a elemet is part of that subset just by igorig some bit positios (which are fixed for a subset). As may other problems i sythesis, this is a NP-hard problem. Thus, for the geeral case, a heuristic has to be used A heuristic for breakpoit ecodig The heuristic we use is drive by the followig assumptios ad facts: Usually, if a breakpoit set A itersects with aother breakpoit set B, the A is a subset of B or vice versa. A subset structure like the oe give i Figure 2 ca be costructed, but it will happe very rarely to that exted, sice the data depedecies usually wo t allow for such differeces i the executio order i differet ifpaths. Breakpoit itersectio ca oly happe i the local cotext of loops if these cotai a hierarchy of if-braches. There caot be a itersectio of breakpoits belogig to differet loops, sice all loop are scheduled idepedetly of each other. This greatly reduces the size of such itersected breakpoit clusters. The algorithm we use is hierarchically orgaized. A level of hierarchy is made up of all itersectig sets, excludig supersets, which belog to the ext higher level, ad subsets, which belog to the ext lower level. Ecodig starts at the highest of hierarchy. We defie Top as the set of all breakpoit sets, Top(S) as the set of all subsets of S. The set of clusters i the set S is C(S), where a cluster C S is give by: N C S M C S { N M N M N N M M} Iit: We set the workig set Curret = Top. Step 1: ( C Curret C( Curret) ): Assig a biary ecodig (the cluster ID). The followig sub-ecodigs of differet clusters are attached to the cluster ID ad ca share the same bit positios. Step 2: Withi each C Curret, all itersectig sets are assiged oe bit. This umber of bits is added to the cluster ID. Step 3: ( C Curret C( Curret) ) ( S C Curret ) : Set Curret = Top(S), ad go to Step 1. The result of this subecodig is attached to the ecodig achieved so far. For differet S, these share the same bit positios. I step 2, we eed the assumptios we made above about the itersectio of breakpoits. E.g. i a case like the oe give i figure 2, the ecodig accordig to step 2 is far away from beig optimal. Here we would eed 8 bits with our heuristic, while a ecodig with 4 bits is possible to fulfil the costraits metioed above. The umber of bits is give by the followig recursive formula: Bits( Top) = log CTop ( ) + max( C Top ) + max( Bits( S C Top )) Bits( ) = 0.

4 The worst case rutime of the algorithm is O(Bp 2 * CS), where Bp deotes the umber of possible differet breakpoits ad CS deotes the umber of cotroller states. This is a equal complexity to state of the art schedulig algorithms sice Bp is proportioal to the umber of operatios. I practice, the rutime is much less, sice there is o global itersectio of breakpoits, thus the algorithm always works locally Example for breakpoit ecodig The example i figure 3 shows a umber of itersectig breakpoits to which we apply our algorithm to clarify its operatio. Bp.1 Bp.2 Bp.7 Bp Bp.4 First, we idetify two clusters at the top level of hierarchy: C1: Bp. 1, 2, 3, 4, 5, 6 C2: Bp. 7, 8 The first bit of the ecodig is the '0' for C1 ad '1' for C2. The ext bits deped o the clusters ad may be shared for differet clusters. For C2 we get oe bit for Bp. 7 o the highest level ad if we go dow oe level, we get a additioal bit for Bp. 8. For cluster 1, we get 2 bits, oe for Bp.1 ad oe for Bp. 2 at the highest level. Goig dow oe level, we get aother 2 bit for Bp. 3 ad Bp. 4, which share the positios with the 2 bit for Bp. 5 ad Bp. 6. Thus, the ecodig of the states is accordig to table1. Bit 1 tells us the cluster, bit 2 idicates that a state belogs to Bp 1, if we are i cluster C1, or to Bp. 7, if we are i cluster C2. The meaig of the other bits is aalog to that. The ecodig eeds 5 bit, which is optimal by chace i this case. It caot be doe with less bits ad still satisfy the ecessary costraits. 3. Breakpoit detectio i the hardware 3.1. Breakpoit detectio logic 5 Bp Bp.68 9 Bp.8 10 Figure 3. Example for the ecodig algorithm State Ecodig Table 1. Example State ecodigs As metioed above, we ca detect breakpoits by comparig the ecoded cotroller state idetifier with a give breakpoit ID. Depedig o the breakpoit we are iterested i, we eed to igore certai bits of the idetifier. The idetifier is a sigal geerated by the cotroller. A example of the VHDL style of the geerated cotroller together with the breakpoit idetifier is show i figure 4. fsmlogic: process(curret_state, iput_list) begi default_assigmets; case curret_state is WHEN state1 => idetifier <= "01010"; output_assigmets; ext_state_assigmet; WHEN state2 => idetifier <= "01000"; output_assigmets; ext_state_assigmet;... ed case; ed process fsmlogic; Figure 4. Breakpoit IDs i the geerated VHDL cotroller It is a process with a case statemet, i which the outputs ad the ext state are computed accordig to the curret state. I each state, we geerate the correspodig idetifier for breakpoit detectio. The breakpoit detectio logic we eed to add to the geerated circuit is show i figure 5. We eed a register to write i the desired breakpoit ad a mask register, where we tell the comparator, which bits of the breakpoit idetifier ad the breakpoit register it should igore. The first bit of the breakpoit register tells the comparator to costatly output '0' if it is set, i.e. it represets the operatio with o breakpoit set. The comparator tests the ot masked bits of the breakpoit idetifier ad the breakpoit register for equality. If equal, the BP-reached sigal is raised to '1'. The BP-scai sigal represets a sca path for settig ad chagig breakpoits i circuit. Thus, we do ot eed to sythesize the

5 BP-ID (from cotroller) BP scai BP-ID reg. circuit each time we wat to set a ew breakpoit. All this is doe dyamically i the implemeted circuit. The detectio of data depedet breakpoits is also very similar to that. We just replace each register i the data path by a compoet which cotais a data register, a programmable register for breakpoit detectio ad a comparator Debuggig cotroller BP-comparator mask reg. BP reached Figure 5. Breapoit detectio logic The programmig of breakpoits ad the iterrupt of the circuit is doe by the debuggig cotroller. This compoet allows the host to cotrol the circuit operatio. A abstract scheme of its operatio is show i figure 6. Write to breakpoit or data path registers Ru circuit Commad Bp_reached or Iterrupt Step oe clock cycle Read data path registers Figure 6. Simplified diagram of dbg. cotroller The macro state commad is the iitial state of the cotroller. I this state it waits for a commad ad the circuit is iterrupted. As commads we ca have the followig: Write a breakpoit Write values to the data path registers Read the cotets of the data path registers Step oe clock cycle Ru the circuit clock outp Each commad is decoded, ad the cotroller switches to the correspodig macro state, which maages the required actio. I each macro state, the cotroller comes back to the commad state after the actio is fiished. Oly i ru_circuit state it remais util a breakpoit is reached or a iterrupt commad is issued by the host. Iterruptig the circuit is doe by settig a clock_cotrol sigal to '0', which the prevets the data path registers ad the circuit cotroller from operatio. Thus, the circuit halts. Each register i the data path is exchaged accordig to figure 7 by a register which allows for the required operatio cotrol. The iputs added to the origial data path register are used for programmig/readback of the register cotets via a sca path (sca_i, sca_e, sca_out), ad for eablig ormal data path operatio (clock_ctrl). These additioal iputs are maaged by the debuggig cotroller. The debuggig cotroller is ot applicatio depedet. We implemet it together with the applicatio o the FPGAs for reasos of simplicity, but it may also reside outside of the programmable logic. 4. Results load ip register sca_i sca_e clock_ctrl clock a) b) load ip register outp sca_out Figure 7. a) Origial ad b) for dbg iserted data path register The described techique is implemeted withi our sythesis tool amed CADDY [5][6][7]. All chages to the circuit cotroller ad to the data path are applied automatically durig the geeratio of the VHDL code by CADDY. There is o maual work by the user required for the geeratio of the debug model. The debuggig cotroller commuicates with a SUN workstatio via the parallel port. The applicatios listed below for the results were all real sythesized, dowloaded ad ru o our WEAVER [8] prototypig board. The followig applicatios were doe: GCD: A stadard example for high level sythesis. SIRDG: A circuit which computes "sigle image radom dot stereograms". It receives a source image with the height iformatio via a parallel iterface ad writes the geerated image to a host via a parallel iterface.

6 DCT: A circuit which computes a two-dimesioal 8x8-DCT. It is implemeted as a coprocessor for the Hyperstoe processor [9]. The commuicatio with the processor is doe via the exteral processor bus. I table 2, the area values of the differet circuits are listed. All values are give i CLBs for the Xilix XC4000 series. Please ote, that the costat overhead for the debuggig cotroller (ca. 44 CLBs) virtually ca be subtracted from the overhead umbers for the examples, because it is ot depedig upo the applicatio. It could easily be implemeted separately from the FPGAs. Circuit Orig. Dbg 1 Dbg 2 Mealy Moore GCD /84 141/ SIRDG / / DCT / / Table 2. Area values Orig. refers to the origial circuit without debug overhead. Dbg 1 icludes the overhead for debuggig without the possibility of data depedet breakpoits. It shows the value with/without debuggig cotroller. Dbg 2 represets the full versio with data depedet breakpoits. Mealy deotes the umber of bits eeded for the Mealy idetifiers, Moore deotes the umber of bits of the Moore idetifiers. The sum of Mealy ad Moore is added to the output of the circuit cotroller. #Bp deotes the umber of differet detectable breakpoits i the specificatio. #States lists the umber of cotroller states of the circuit cotroller. Table 3 shows the delay that is added by the additioal logic for debuggig. Circuit Orig. Dbg 1 Dbg 2 GCD 5.7 Mhz 4.7 Mhz 4.5 Mhz SIRDG 7.7 Mhz 4.8 Mhz 4.9 Mhz DCT 3.5 Mhz 3.5 Mhz 3.5 Mhz Table 3. Clock frequecies The values are obtaied by the Xilix tool 'xdelay'. This tool provides a quite pessimistic estimatio. All desigs ru at a sigificatly higher clock speed o our Weaver board. Nevertheless, it shows the relatio betwee the differet implemetatios. There is ot much differece betwee the two versios with debuggig overhead, sice the evaluatio of coditioal breakpoits does ot slow dow the circuit additioally. Our approach maily adds cotroller delay. Therefore we do ot add ay delay to the dct, which is maily determied by the combiatorial multiplier. To this path, we do o add ay delay. Therefore, the debug versios ca be clocked with equal frequecy. 5. Summary I this paper, we have preseted the details of how we hadle breakpoits i our ew SLE approach. We have discussed, how we ca relate breakpoits set i a algorithmic specificatio to a implemeted circuit. We have show, how we ecode the breakpoits ad how we detect them by hardware meas. We have demostrated the applicability of our approach by a set of implemeted circuits. 6. Refereces [1] Y. Taurha, S. Schmerler, K. Mueller-Glaser, A Backplae Approach for Cosimulatio i High-Level System Specificatio Eviromets, Europea Desig Automatio Coferece EURODAC 95, Brighto, 1995 [2] J. Soiie, T. Huttue, K. Tiesyrjae, H. Heusala, Cosimulatio of Real-Time Cotrol Systems, Europea Desig Automatio Coferece EURODAC 95, Brighto, 1995 [3] H. Owe, U. Kah, J. Hughes, FPGA based ASIC Hardware Emulator Architectures, School of Electrical ad Computer Egieerig, Georgia Istitute of Techology, 1993 [4] ASIC-Emulatio auf RTL-Level, Markt&Techik - Wochezeitug für Elektroik Nr. 42, 1994 [5] R. Camposao, W. Rosestiel, Sythesizig Circuits from Behavioral Descriptios, IEEE Trasactios o CAD, Vol. 8, [6] P. Gutberlet, J. Müller, H. Krämer, W. Rosestiel, Automatic Module Allocatio i High Level Sythesis, Europea Desig Automatio Coferece EURODAC 92, Hamburg, 1992 [7] P. Gutberlet, W. Rosestiel, Schedulig Betwee Basic Blocks i the CADDY Sythesis System, Europea Coferece o Desig Automatio EDAC 92, Brussels, 1992 [8] G. Koch, U. Kebschull, W. Rosestiel, A Prototypig Architecture for Hardware/Software Codesig i the COBRA Project, Proceedigs of 3rd iteratioal Workshop o Hardware/Software Codesig Codes/CASHE 94, Greoble 1994 [9] Hyperstoe electroics, Hyperstoe E1 32-Bit-Microprocessor User s Maual, 1990 [10] G. Koch, U. Kebschull, W. Rosestiel, Debuggig of Behavioral VHDL Specificatios by Source Level Emulatio, Europea Desig Automatio Coferece EURO- DAC 95, Brighto, 1995

Elementary Educational Computer

Elementary Educational Computer Chapter 5 Elemetary Educatioal Computer. Geeral structure of the Elemetary Educatioal Computer (EEC) The EEC coforms to the 5 uits structure defied by vo Neuma's model (.) All uits are preseted i a simplified

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

Module Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two

Module Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two Give submodule mux32two: 2-to- MUX module mux32two (iput [3:] i,i, iput sel, output [3:] out); Module Istatiatio Fiite Machies esig methodology for sequetial logic -- idetify distict s -- create trasitio

More information

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

Appendix D. Controller Implementation

Appendix D. Controller Implementation COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Appedix D Cotroller Implemetatio Cotroller Implemetatios Combiatioal logic (sigle-cycle); Fiite state machie (multi-cycle, pipelied);

More information

Lecture 3. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram

Lecture 3. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram Lecture 3 RTL Desig Methodology Trasitio from Pseudocode & Iterface to a Correspodig Block Diagram Structure of a Typical Digital Data Iputs Datapath (Executio Uit) Data Outputs System Cotrol Sigals Status

More information

EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control

EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control EE 459/500 HDL Based Digital Desig with Programmable Logic Lecture 13 Cotrol ad Sequecig: Hardwired ad Microprogrammed Cotrol Refereces: Chapter s 4,5 from textbook Chapter 7 of M.M. Mao ad C.R. Kime,

More information

Lecture 2. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram

Lecture 2. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram Lecture 2 RTL Desig Methodology Trasitio from Pseudocode & Iterface to a Correspodig Block Diagram Structure of a Typical Digital Data Iputs Datapath (Executio Uit) Data Outputs System Cotrol Sigals Status

More information

End Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization

End Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization Ed Semester Examiatio 2013-14 CSE, III Yr. (I Sem), 30002: Computer Orgaizatio Istructios: GROUP -A 1. Write the questio paper group (A, B, C, D), o frot page top of aswer book, as per what is metioed

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

One advantage that SONAR has over any other music-sequencing product I ve worked

One advantage that SONAR has over any other music-sequencing product I ve worked *gajedra* D:/Thomso_Learig_Projects/Garrigus_163132/z_productio/z_3B2_3D_files/Garrigus_163132_ch17.3d, 14/11/08/16:26:39, 16:26, page: 647 17 CAL 101 Oe advatage that SONAR has over ay other music-sequecig

More information

Chapter 4 The Datapath

Chapter 4 The Datapath The Ageda Chapter 4 The Datapath Based o slides McGraw-Hill Additioal material 24/25/26 Lewis/Marti Additioal material 28 Roth Additioal material 2 Taylor Additioal material 2 Farmer Tae the elemets that

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON

A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON Roberto Lopez ad Eugeio Oñate Iteratioal Ceter for Numerical Methods i Egieerig (CIMNE) Edificio C1, Gra Capitá s/, 08034 Barceloa, Spai ABSTRACT I this work

More information

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro

More information

Software development of components for complex signal analysis on the example of adaptive recursive estimation methods.

Software development of components for complex signal analysis on the example of adaptive recursive estimation methods. Software developmet of compoets for complex sigal aalysis o the example of adaptive recursive estimatio methods. SIMON BOYMANN, RALPH MASCHOTTA, SILKE LEHMANN, DUNJA STEUER Istitute of Biomedical Egieerig

More information

Data diverse software fault tolerance techniques

Data diverse software fault tolerance techniques Data diverse software fault tolerace techiques Complemets desig diversity by compesatig for desig diversity s s limitatios Ivolves obtaiig a related set of poits i the program data space, executig the

More information

The Magma Database file formats

The Magma Database file formats The Magma Database file formats Adrew Gaylard, Bret Pikey, ad Mart-Mari Breedt Johaesburg, South Africa 15th May 2006 1 Summary Magma is a ope-source object database created by Chris Muller, of Kasas City,

More information

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation Improvemet of the Orthogoal Code Covolutio Capabilities Usig FPGA Implemetatio Naima Kaabouch, Member, IEEE, Apara Dhirde, Member, IEEE, Saleh Faruque, Member, IEEE Departmet of Electrical Egieerig, Uiversity

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 19 Query Optimizatio Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Query optimizatio Coducted by a query optimizer i a DBMS Goal:

More information

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 4 Procedural Abstractio ad Fuctios That Retur a Value Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 4.1 Top-Dow Desig 4.2 Predefied Fuctios 4.3 Programmer-Defied Fuctios 4.4

More information

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig

More information

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

Baan Tools User Management

Baan Tools User Management Baa Tools User Maagemet Module Procedure UP008A US Documetiformatio Documet Documet code : UP008A US Documet group : User Documetatio Documet title : User Maagemet Applicatio/Package : Baa Tools Editio

More information

Chapter 3 Classification of FFT Processor Algorithms

Chapter 3 Classification of FFT Processor Algorithms Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As

More information

Task scenarios Outline. Scenarios in Knowledge Extraction. Proposed Framework for Scenario to Design Diagram Transformation

Task scenarios Outline. Scenarios in Knowledge Extraction. Proposed Framework for Scenario to Design Diagram Transformation 6-0-0 Kowledge Trasformatio from Task Scearios to View-based Desig Diagrams Nima Dezhkam Kamra Sartipi {dezhka, sartipi}@mcmaster.ca Departmet of Computig ad Software McMaster Uiversity CANADA SEKE 08

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

BAAN IVc/BaanERP. Conversion Guide Oracle7 to Oracle8

BAAN IVc/BaanERP. Conversion Guide Oracle7 to Oracle8 BAAN IVc/BaaERP A publicatio of: Baa Developmet B.V. P.O.Box 143 3770 AC Bareveld The Netherlads Prited i the Netherlads Baa Developmet B.V. 1999. All rights reserved. The iformatio i this documet is subject

More information

3D Model Retrieval Method Based on Sample Prediction

3D Model Retrieval Method Based on Sample Prediction 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer

More information

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 )

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 ) EE26: Digital Desig, Sprig 28 3/6/8 EE 26: Itroductio to Digital Desig Combiatioal Datapath Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Combiatioal Logic Blocks Multiplexer Ecoders/Decoders

More information

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 12: Virtual Memory Prof. Yajig Li Uiversity of Chicago A System with Physical Memory Oly Examples: most Cray machies early PCs Memory early all embedded systems

More information

Security of Bluetooth: An overview of Bluetooth Security

Security of Bluetooth: An overview of Bluetooth Security Versio 2 Security of Bluetooth: A overview of Bluetooth Security Marjaaa Träskbäck Departmet of Electrical ad Commuicatios Egieerig mtraskba@cc.hut.fi 52655H ABSTRACT The purpose of this paper is to give

More information

COP4020 Programming Languages. Compilers and Interpreters Prof. Robert van Engelen

COP4020 Programming Languages. Compilers and Interpreters Prof. Robert van Engelen COP4020 mig Laguages Compilers ad Iterpreters Prof. Robert va Egele Overview Commo compiler ad iterpreter cofiguratios Virtual machies Itegrated developmet eviromets Compiler phases Lexical aalysis Sytax

More information

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig

More information

Τεχνολογία Λογισμικού

Τεχνολογία Λογισμικού ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Τεχνολογία Λογισμικού, 7ο/9ο εξάμηνο 2018-2019 Τεχνολογία Λογισμικού Ν.Παπασπύρου, Αν.Καθ. ΣΗΜΜΥ, ickie@softlab.tua,gr

More information

A collection of open-sourced RISC-V processors

A collection of open-sourced RISC-V processors Riscy Processors A collectio of ope-sourced RISC-V processors Ady Wright, Sizhuo Zhag, Thomas Bourgeat, Murali Vijayaraghava, Jamey Hicks, Arvid Computatio Structures Group, CSAIL, MIT 4 th RISC-V Workshop

More information

Exceptions. Your computer takes exception. The Exception Class. Causes of Exceptions

Exceptions. Your computer takes exception. The Exception Class. Causes of Exceptions Your computer takes exceptio s s are errors i the logic of a program (ru-time errors). Examples: i thread mai java.io.filenotfoud: studet.txt (The system caot fid the file specified.) i thread mai java.lag.nullpoiter:

More information

L6: FSMs and Synchronization

L6: FSMs and Synchronization L6: FSMs ad Sychroizatio Ackowledgemets: Materials i this lecture are courtesy of the followig sources ad are used with permissio. Rex Mi J. Rabaey, A. Chadrakasa, B. Nikolic. igital Itegrated Circuits:

More information

CMSC Computer Architecture Lecture 3: ISA and Introduction to Microarchitecture. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 3: ISA and Introduction to Microarchitecture. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 3: ISA ad Itroductio to Microarchitecture Prof. Yajig Li Uiversity of Chicago Lecture Outlie ISA uarch (hardware implemetatio of a ISA) Logic desig basics Sigle-cycle

More information

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5 Morga Kaufma Publishers 26 February, 28 COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 5 Set-Associative Cache Architecture Performace Summary Whe CPU performace icreases:

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

Lower Bounds for Sorting

Lower Bounds for Sorting Liear Sortig Topics Covered: Lower Bouds for Sortig Coutig Sort Radix Sort Bucket Sort Lower Bouds for Sortig Compariso vs. o-compariso sortig Decisio tree model Worst case lower boud Compariso Sortig

More information

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 22 Database Recovery Techiques Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Recovery algorithms Recovery cocepts Write-ahead

More information

Evaluation scheme for Tracking in AMI

Evaluation scheme for Tracking in AMI A M I C o m m u i c a t i o A U G M E N T E D M U L T I - P A R T Y I N T E R A C T I O N http://www.amiproject.org/ Evaluatio scheme for Trackig i AMI S. Schreiber a D. Gatica-Perez b AMI WP4 Trackig:

More information

Digital System Design

Digital System Design July, 22 9:55 vra235_ch Sheet umber Page umber 65 black chapter Digital System Desig a b c d e f g h 8 7 6 5 4 3 2. Bd3 g6+, Ke8 d8 65 July, 22 9:55 vra235_ch Sheet umber 2 Page umber 66 black 66 CHAPTER

More information

Bayesian approach to reliability modelling for a probability of failure on demand parameter

Bayesian approach to reliability modelling for a probability of failure on demand parameter Bayesia approach to reliability modellig for a probability of failure o demad parameter BÖRCSÖK J., SCHAEFER S. Departmet of Computer Architecture ad System Programmig Uiversity Kassel, Wilhelmshöher Allee

More information

Computers and Scientific Thinking

Computers and Scientific Thinking Computers ad Scietific Thikig David Reed, Creighto Uiversity Chapter 15 JavaScript Strigs 1 Strigs as Objects so far, your iteractive Web pages have maipulated strigs i simple ways use text box to iput

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19 CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS APPLICATION NOTE PACE175AE BUILT-IN UNCTIONS About This Note This applicatio brief is iteded to explai ad demostrate the use of the special fuctios that are built ito the PACE175AE processor. These powerful

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets

More information

K-NET bus. When several turrets are connected to the K-Bus, the structure of the system is as showns

K-NET bus. When several turrets are connected to the K-Bus, the structure of the system is as showns K-NET bus The K-Net bus is based o the SPI bus but it allows to addressig may differet turrets like the I 2 C bus. The K-Net is 6 a wires bus (4 for SPI wires ad 2 additioal wires for request ad ackowledge

More information

1 Enterprise Modeler

1 Enterprise Modeler 1 Eterprise Modeler Itroductio I BaaERP, a Busiess Cotrol Model ad a Eterprise Structure Model for multi-site cofiguratios are itroduced. Eterprise Structure Model Busiess Cotrol Models Busiess Fuctio

More information

Structuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software

Structuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software Structurig Redudacy for Fault Tolerace CSE 598D: Fault Tolerat Software What do we wat to achieve? Versios Damage Assessmet Versio 1 Error Detectio Iputs Versio 2 Voter Outputs State Restoratio Cotiued

More information

BOOLEAN DIFFERENTIATION EQUATIONS APPLICABLE IN RECONFIGURABLE COMPUTATIONAL MEDIUM

BOOLEAN DIFFERENTIATION EQUATIONS APPLICABLE IN RECONFIGURABLE COMPUTATIONAL MEDIUM MATEC Web of Cofereces 79, 01014 (016) DOI: 10.1051/ mateccof/0167901014 T 016 BOOLEAN DIFFERENTIATION EQUATIONS APPLICABLE IN RECONFIGURABLE COMPUTATIONAL MEDIUM Staislav Shidlovskiy 1, 1 Natioal Research

More information

ICS Regent. Communications Modules. Module Operation. RS-232, RS-422 and RS-485 (T3150A) PD-6002

ICS Regent. Communications Modules. Module Operation. RS-232, RS-422 and RS-485 (T3150A) PD-6002 ICS Reget Commuicatios Modules RS-232, RS-422 ad RS-485 (T3150A) Issue 1, March, 06 Commuicatios modules provide a serial commuicatios iterface betwee the cotroller ad exteral equipmet. Commuicatios modules

More information

CMSC Computer Architecture Lecture 2: ISA. Prof. Yanjing Li Department of Computer Science University of Chicago

CMSC Computer Architecture Lecture 2: ISA. Prof. Yanjing Li Department of Computer Science University of Chicago CMSC 22200 Computer Architecture Lecture 2: ISA Prof. Yajig Li Departmet of Computer Sciece Uiversity of Chicago Admiistrative Stuff Lab1 out toight Due Thursday (10/18) Lab1 review sessio Tomorrow, 10/05,

More information

An Improved Shuffled Frog-Leaping Algorithm for Knapsack Problem

An Improved Shuffled Frog-Leaping Algorithm for Knapsack Problem A Improved Shuffled Frog-Leapig Algorithm for Kapsack Problem Zhoufag Li, Ya Zhou, ad Peg Cheg School of Iformatio Sciece ad Egieerig Hea Uiversity of Techology ZhegZhou, Chia lzhf1978@126.com Abstract.

More information

Review: The ACID properties

Review: The ACID properties Recovery Review: The ACID properties A tomicity: All actios i the Xactio happe, or oe happe. C osistecy: If each Xactio is cosistet, ad the DB starts cosistet, it eds up cosistet. I solatio: Executio of

More information

MR-2010I %MktBSize Macro 989. %MktBSize Macro

MR-2010I %MktBSize Macro 989. %MktBSize Macro MR-2010I %MktBSize Macro 989 %MktBSize Macro The %MktBSize autocall macro suggests sizes for balaced icomplete block desigs (BIBDs). The sizes that it reports are sizes that meet ecessary but ot sufficiet

More information

Behavioral Modeling in Verilog

Behavioral Modeling in Verilog Behavioral Modelig i Verilog COE 202 Digital Logic Desig Dr. Muhamed Mudawar Kig Fahd Uiversity of Petroleum ad Mierals Presetatio Outlie Itroductio to Dataflow ad Behavioral Modelig Verilog Operators

More information

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 18 Strategies for Query Processig Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio DBMS techiques to process a query Scaer idetifies

More information

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup   Note: Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

Empirical Validate C&K Suite for Predict Fault-Proneness of Object-Oriented Classes Developed Using Fuzzy Logic.

Empirical Validate C&K Suite for Predict Fault-Proneness of Object-Oriented Classes Developed Using Fuzzy Logic. Empirical Validate C&K Suite for Predict Fault-Proeess of Object-Orieted Classes Developed Usig Fuzzy Logic. Mohammad Amro 1, Moataz Ahmed 1, Kaaa Faisal 2 1 Iformatio ad Computer Sciece Departmet, Kig

More information

Automatic Generation of Polynomial-Basis Multipliers in GF (2 n ) using Recursive VHDL

Automatic Generation of Polynomial-Basis Multipliers in GF (2 n ) using Recursive VHDL Automatic Geeratio of Polyomial-Basis Multipliers i GF (2 ) usig Recursive VHDL J. Nelso, G. Lai, A. Teca Abstract Multiplicatio i GF (2 ) is very commoly used i the fields of cryptography ad error correctig

More information

A Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions

A Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions Proceedigs of the 10th WSEAS Iteratioal Coferece o APPLIED MATHEMATICS, Dallas, Texas, USA, November 1-3, 2006 316 A Geeralized Set Theoretic Approach for Time ad Space Complexity Aalysis of Algorithms

More information

IMP: Superposer Integrated Morphometrics Package Superposition Tool

IMP: Superposer Integrated Morphometrics Package Superposition Tool IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

Avid Interplay Bundle

Avid Interplay Bundle Avid Iterplay Budle Versio 2.5 Cofigurator ReadMe Overview This documet provides a overview of Iterplay Budle v2.5 ad describes how to ru the Iterplay Budle cofiguratio tool. Iterplay Budle v2.5 refers

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

6.854J / J Advanced Algorithms Fall 2008

6.854J / J Advanced Algorithms Fall 2008 MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

Chapter 4 Threads. Operating Systems: Internals and Design Principles. Ninth Edition By William Stallings

Chapter 4 Threads. Operating Systems: Internals and Design Principles. Ninth Edition By William Stallings Operatig Systems: Iterals ad Desig Priciples Chapter 4 Threads Nith Editio By William Stalligs Processes ad Threads Resource Owership Process icludes a virtual address space to hold the process image The

More information

Bluespec-3: Modules & Interfaces. Bluespec: State and Rules organized into modules

Bluespec-3: Modules & Interfaces. Bluespec: State and Rules organized into modules Bluespec-3: Modules & Iterfaces Arvid Computer Sciece & Artificial Itelligece Lab Massachusetts Istitute of Techology Based o material prepared by Bluespec Ic, Jauary 2005 February 28, 2005 L09-1 Bluespec:

More information

CAEN Tools for Discovery

CAEN Tools for Discovery Applicatio Note AN2086 Sychroizatio of CAEN Digitizers i Multiple Board Acquisitio Systems Viareggio 9 May 2013 Itroductio High speed digitizers fid applicatios i several fields ragig from the idustry

More information

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen COP4020 Programmig Laguages Fuctioal Programmig Prof. Robert va Egele Overview What is fuctioal programmig? Historical origis of fuctioal programmig Fuctioal programmig today Cocepts of fuctioal programmig

More information

WYSE Academic Challenge Sectional Computer Science 2005 SOLUTION SET

WYSE Academic Challenge Sectional Computer Science 2005 SOLUTION SET WYSE Academic Challege Sectioal Computer Sciece 2005 SOLUTION SET 1. Correct aswer: a. Hz = cycle / secod. CPI = 2, therefore, CPI*I = 2 * 28 X 10 8 istructios = 56 X 10 8 cycles. The clock rate is 56

More information

BOOLEAN MATHEMATICS: GENERAL THEORY

BOOLEAN MATHEMATICS: GENERAL THEORY CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.

More information

Analysis of Algorithms

Analysis of Algorithms Presetatio for use with the textbook, Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Aalysis of Algorithms Iput 2015 Goodrich ad Tamassia Algorithm Aalysis of Algorithms

More information

Algorithms for Disk Covering Problems with the Most Points

Algorithms for Disk Covering Problems with the Most Points Algorithms for Disk Coverig Problems with the Most Poits Bi Xiao Departmet of Computig Hog Kog Polytechic Uiversity Hug Hom, Kowloo, Hog Kog csbxiao@comp.polyu.edu.hk Qigfeg Zhuge, Yi He, Zili Shao, Edwi

More information

ISSN (Print) Research Article. *Corresponding author Nengfa Hu

ISSN (Print) Research Article. *Corresponding author Nengfa Hu Scholars Joural of Egieerig ad Techology (SJET) Sch. J. Eg. Tech., 2016; 4(5):249-253 Scholars Academic ad Scietific Publisher (A Iteratioal Publisher for Academic ad Scietific Resources) www.saspublisher.com

More information

Sorting in Linear Time. Data Structures and Algorithms Andrei Bulatov

Sorting in Linear Time. Data Structures and Algorithms Andrei Bulatov Sortig i Liear Time Data Structures ad Algorithms Adrei Bulatov Algorithms Sortig i Liear Time 7-2 Compariso Sorts The oly test that all the algorithms we have cosidered so far is compariso The oly iformatio

More information

COMP Parallel Computing. PRAM (1): The PRAM model and complexity measures

COMP Parallel Computing. PRAM (1): The PRAM model and complexity measures COMP 633 - Parallel Computig Lecture 2 August 24, 2017 : The PRAM model ad complexity measures 1 First class summary This course is about parallel computig to achieve high-er performace o idividual problems

More information

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1 COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,

More information