CPE 628 Chapter 2 Design for Testability. Dr. Rhonda Kay Gaede UAH. UAH Chapter Introduction
|
|
- Eric Reed
- 5 years ago
- Views:
Transcription
1 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 connected nto a, s currently the most popular DFT approach Scan can be or, more promnent now Scan s now the bass for (BIST) technques Recently, DFT s mgratng from to level Page 2
2 22 Testablty Analyss Test programs use to ad n makng decsons Types of testablty analyss Page 3 22 Testablty Analyss SCOAP For each n a logc crcut, the followng values are calculated CC(s) combnatonal of s CC(s) combnatonal of s CO(s) combnatonal of s SC(s) sequental of s SC(s) sequental of s SO(s) sequental of s CC = CC = for a SC = SC = for a CO = SO = for a Page 4 2
3 22 Testablty Analyss SCOAP (Combnatonal Calculatons) Assgn order for each Calculate for all sgnals startng wth ( ), proceedng n order ( ) Page 5 22 Testablty Analyss SCOAP (Combnatonal Calculatons) Startng from, calculate Page 6 3
4 22 Testablty Analyss SCOAP (Combnatonal Calculatons) A B Sum C out C n Page 7 22 Testablty Analyss Probablty-Based For each sgnal s n a combnatonal crcut, calculate CO(s) - -controllablty of s C(s) - -controllablty of s O(s) - observablty of s C(s) and C(s) are the probabltes of controllng s to and from prmary nputs, respectvely O(s) s the probablty of observng sgnal s at outputs C = C = for a PI + = Page 8 4
5 22 Testablty Analyss Probablty- Based (Controllablty Calculaton Rules) Prmary Input AND OR NOT NAND NOR BUFFER XOR XNOR Branch -controllablty (Prmary nput, output, branch) p (output -controllablty) Π (nput -controllabltes) Input -controllablty Π (nput -controllabltes) (output -controllablty) Input -controllablty -controllablty -controllablty Stem -controllablty -controllablty (Prmary nput, output, branch) p = - p Π (nput -controllabltes) (output -controllablty) Input -controllablty (output -controllablty) Π (nput -controllabltes) Input -controllablty Σ(C(a) C(b), C(a) C(b)) Σ(C(a) C(b), C(a) C(b)) Stem -controllablty Page 9 UAH Chapter 2 22 Testablty Analyss Probablty- Based (Observablty Calculaton Rules) Page 5
6 22 Testablty Analyss Probablty- Based (Comparson to SCOAP) Sgnals wth probablty-based testablty measures tend to be dffcult to test wth or test patterns The faults on these lnes are often referred to as - Page 22 Testablty Analyss Smulaton-Based Smulaton-based methods use for testablty analyss and can be used as an to or a to -based analyss A sample set of nput test patterns are selected that are ether generated or derved from a s conducted to collect the of all or part of sgnal lnes of nterest Occurrences of, Occurrences of, transtons Due to long run tmes, ths technque s used mostly for - or - applcatons Page 2 6
7 22 Testablty Analyss RTL Improve testablty Improve the testablty of a - logc BIST crcut Lead to more results The number of s much less Become more tme Much than an equvalent -level model Page 3 22 Testablty Analyss RTL(Example) a b a b a n- b n- c c c c + c n- c out s n s s s n- Rpple Carry Adder Page 4 7
8 22 Testablty Analyss RTL(Example--Controllablty) The probablty-based -controllablty measures of s and c +, denoted by C(s ) and C(c +), are calculated as follows: C(s ) = α + C(c ) - 2 ( α C(c )) C(c + ) = α C(c ) + C(a ) C(b ) α = C(a ) + C(b ) - 2 C(a ) C(b ) α s the probablty that (a b ) = C(s ) s the probablty that (a b c ) = Page 5 22 Testablty Analyss RTL(Example- -Controllablty & Observablty) The probablty-based -controllablty of each output l, denoted by C(l), n the n-bt rpple-carry adder s O(l, s ) s defned as the probablty that a sgnal change on l wll result n a sgnal change on s O(a, s ) = O(b, s ) = O(c, s ) = O(s ) Snce where =,,, n - Harder for Page 6 8
9 23 Desgn for Testablty Bascs DFT (lterally ) Effects are and not Not Dffcult to A DFT Easly and Yelds the results Easy to Page 7 23 Desgn for Testablty Bascs Ad Hoc Approach Typcal Ad Hoc DFT technques Insert ponts Avod for storage elements Avod combnatonal Avod logc Avod logc a large crcut nto Page 8 9
10 23 Desgn for Testablty Bascs Ad Hoc Technques(Observablty Test Ponts) Logc crcut Low-observablty node A Low-observablty node B Low-observablty node C OP OP 2 OP 3 SO D SO SO OP_output OP2 shows the structure of an, whch s composed of a Observaton shft regster multplexer (MUX) and a D flp-flop Page 9 23 Desgn for Testablty Bascs Ad Hoc Technques(Controllablty Test Ponts) A MUX s nserted between the source and destnaton ends Durng normal operaton, TM =, such that the value from the source end drves the destnaton end through the port of the MUX Durng test, TM = such that the value from the D flp-flop drves the destnaton end through the port of the MUX Page 2
11 23 Desgn for Testablty Bascs Structured Approach X Combnatonal logc Y Y 2 X 2 X 3 f FF 3 D FF 2 D FF D To detect a fault, f Shft the desred Apply desred values on Apply one and fault effect Shft (shft n next test stmulus for ) Page 2 24 Scan Cell Desgns Muxed-D Scan Cell(Flp-Flop) D /SO D D 2 D 3 D 4 T T 2 T 3 T 4 Edge-trggered muxed-d scan cell /SO D T 3 Edge-trggered muxed-d scan cell operaton Page 22
12 24 Scan Cell Desgns Muxed-D Scan Cell(Latch) D D Level-senstve/edgetrggered muxed-d scan cell desgn SO Ths scan cell s composed of a multplexer, a D latch, and a D flp-flop In ths case, operaton s conducted n an manner, whle operaton and operaton s conducted n a manner Page Scan Cell Desgns Clocked Scan Cell /SO D S Clocked-scan cell Clocked-scan cell operaton Page 24 2
13 24 Scan Cell Desgns LSSD Scan Cell D L C I A B SRL L 2 C +L A B +L D 2 I +L D D 2 D 3 D 4 T T 2 T 3 T 4 D T 3 Modes of Operaton Normal Fed by, clocks Fed by, clocks to, clocks data to Shft clocks to, clocks to +L 2 T 3 Page Scan Cell Desgns Comparson Advantages Dsadvantages Muxed-D Scan Cell Clocked-Scan Cell LSSD Scan Cell to modern desgns provded by desgn automaton tools No degradaton Insert scan nto a desgn Guaranteed to be Add a multplexer Requre addtonal routng Increase complexty Page 26 3
14 25 Scan Archtectures Full-Scan Desgn or storage elements are converted nto and ATPG s used for test generaton Partal-Scan Desgn A of storage elements are converted nto and ATPG s typcally used for test generaton Random-Access Scan Desgn A random addressng mechansm, nstead of scan chans, s used to provde drect access to read or wrte scan cell Page Scan Archtectures Full-Scan Desgn All storage elements are replaced wth scan cells All can be All can be Advantage: Converts ATPG nto ATPG Almost full-scan desgn A percentage of storage elements are not replaced wth scan cells For performance reasons Storage elements that le on paths For functonal reasons Storage elements drven by a small that are deemed too to be worth the scan nserton effort Page 28 4
15 25 Scan Archtectures Full-Scan Desgn(Muxed-D) PI PPI X X 2 X 3 SFF Combnatonal logc Y Y2 SFF 2 SFF 3 SO Pseudo prmary outputs (PPOs) the scan cell can be are observed through outputs PO PPO Prmary nputs (PIs) nputs can be set to logc values set drectly n Pseudo prmary nputs (PPIs) the scan cell can be set to logc values are set through scan chan Prmary outputs (POs) outputs can be are observed drectly n Page Scan Archtectures Full-Scan Desgn(Muxed-D) PI SFF SFF 2 SFF 3 X X V : PI V 2 : PI S H C H S H C X L L H H L L L H L L L H V : PPI PO observaton PPO observaton V 2 : PPI S: shft operaton / C: capture operaton / H: hold cycle Test Operatons Page 3 5
16 25 Scan Archtectures Full-Scan Desgn(Clocked) PI X X 2 X 3 Combnatonal logc Y Y2 PPI PPO SFF SFF 2 SFF 3 SO D S D S D S D S PO In a muxed-d fullscan crcut, a scan enable sgnal s used In a clocked full-scan desgn, two operatons are dstngushed by properly applyng the two ndependent clocks S and D durng shft mode and capture mode Page 3 25 Scan Archtectures Full-Scan Desgn(LSSD) C A B C 2 X Combnatonal logc Combnatonal logc 2 Y X 2 2 SRL D I +L 2 C A +L B X 3 Y SRL 2 D I +L 2 C A +L B Sngle Latch Desgn SRL 3 D I +L 2 SO A +L C B The output port +L of the latch L s used to drve the combnatonal logc of the desgn In ths case, the latch L 2 s only used for scan testng To prevent combnatonal feedback, s fed by only, s fed by only Page 32 6
17 25 Scan Archtectures Full-Scan Desgn(LSSD) X X 2 X 3 C A C 2 or B SRL D I +L 2 C A +L B Combnatonal logc SRL 2 D I +L 2 C A +L B SRL 3 D I +L 2 C A +L B Y Y 2 SO In mode, use and Durng the operaton, use and Durng the operaton, use and Double Latch Desgn Page Scan Archtectures Partal-Scan Desgn PI PPI X X 2 X 3 Combnatonal logc SFF FF 2 SFF 3 Partal-Scan Desgn Y Y2 PO PPO SO Advantages Reduces and Dsadvantages Results n and generaton Offers less support for, compared to full-scan Page 34 7
18 25 Scan Archtectures Random-Access Scan Desgn Full-scan and partal scan are both scan Advantage low overhead of Dsadvantages Cannot control cells Hgh swtchng actvty at scan cells leads to hgh, resultng n,, or even - scan (RAS) ams to allevate these ssues Each cell s and addressable PI Row (X) decoder Combnatonal logc SC SC SC SC SC SC SC SC SC Column (Y) decoder Address shft regster PO S SO AI Page Scan Archtectures Random-Access Scan Desgn(Progressve) RAS sgnfcantly reduces test and smplfes ndependent applcaton Dsadvantage s to acheve random access Also, no guarantee of reduced random-access scan has been proposed to allevate these problems RE D SD Ф Ф Ф SD Ф Ф Ф PRAS scan cell desgn In mode, all horzontal row enable (RE) sgnals are set to, forcng each cell to act as a D flp-flop In mode, to capture the response from D, the RE sgnal s set to and a pulse s appled on clock, whch causes the value on D to be nto the scan cell Page 36 8
19 25 Scan Archtectures Random-Access Scan Desgn(Progressve) Row enable shft regster TM Test /SO control logc Sense-amplfers & MISR SC SC Column lne drvers SC SC SC SC SC SC SC Column address decoder CA PRAS Archtecture PO Combnatonal logc PI for each test vector v ( =, 2,, N){ /* Test stmulus applcaton */ /* Test response compresson */ enable TM; for each row r j (j =, 2,, m) { read all scan cells n r j / update MISR; for each scan cell SC n r j /* v(sc): current value of SC */ /* v (SC): value of SC n v */ f v(sc) v (SC) update SC; } /* Test response acquston */ dsable TM; apply the normal clock; } scan-out MISR as the fnal test response; Page Scan Desgn Rules Problematc I/O Ports Clocks Clocks Feedback Loops Set/Reset Sgnals Page 38 9
20 26 Scan Desgn Rules Trstate Buses Problem wth may occur durng Fault wthout, or SFF EN D SFF EN Bus keeper Functonal enable logc SFF 2 EN 2 D 2 Bus Functonal enable logc SFF 2 EN 2 D D 2 Bus SFF 3 EN 3 D 3 SFF 3 EN 3 D 3 Page Scan Desgn Rules Bdrectonal I/O Ports Problem may occur durng shftng Pn s nput but shfted value may enable BO BO BI Orgnal Crcut I/O BO BI I/O Modfed Crcut Page 4 2
21 26 Scan Desgn Rules Gated Clocks D D Clock EN gatng logc LAT Orgnal Crcut CEN D G If s used, only durng If s used, fault coverage on the s lost A G TM or DFF D D D Clock gatng reduces consumpton but also means that the clock cannot be from Clock gatng logc EN LAT Modfed Crcut CEN D G B G DFF D Page 4 26 Scan Desgn Rules Derved Clocks D I DFF D DFF 2 D A derved clock s a clock sgnal and s not controllable from the Orgnal Crcut A derved clock must be durng the operaton TM I D DFF D DFF 2 D Modfed Crcut Page 42 2
22 26 Scan Desgn Rules Combnatonal Feedback Loops Combnatonal logc Ḍ S Orgnal Crcut D Get rd of t f at all possble Combnatonal logc TM S Modfed Crcut Page Scan Desgn Rules Asynchronous Set/Reset Sgnals SFF RL Orgnal Crcut Usng means that faults n the logc cannot be Usng may lead to Use, R SFF 2 Asynchronous set/reset sgnals not from the prmary nputs can prevent from workng properly TM SFF RL Modfed Crcut R SFF 2 Page 44 22
23 26 Scan Desgn Flow Scan Desgn Rule Checkng and Repar and all scan desgn rule Scan Synthess Converts a desgn nto a desgn wthout orgnal functonalty Scan Extracton all scan cell nstances from all specfed Scan Verfcaton Verfy operaton and operaton usng tmng fle n standard delay format (SDF) Constrant & control nformaton Orgnal desgn Scan desgn rule checkng and repar Testable desgn Scan synthess Scan confguraton Scan replacement Scan reorderng Layout nformaton Scan sttchng Scan desgn Scan extracton Test generaton Scan verfcaton Page Scan Desgn Flow Scan Desgn Rule Checkng and Repar CD CCD CCD 2 2 CD 2 CD 3 CCD 3 CCD 4 CD 4 CD 5 CD 6 CD 7 3 CCD 5 Durng the phase, followng desgn rules should guarantee correctness for data paths that and wthn the same Movng across s trcky, but can some The clock between scan cells must be properly n order not to cause any Page 46 23
24 27 Scan Desgn Flow Scan Synthess Includes four separate and dstnct steps: Scan Confguraton The number of used The of scan cells used to these scan chans Whch storage elements to from the process How the scan cells are Scan Replacement Replaces all storage elements n the testable desgn wth ther scan cells Scan Reorderng The process of reorderng the scan chans based on the scan cell, n order to mnmze the amount of used to mplement the scan chans Scan Sttchng Sttch all together to form Page Scan Desgn Flow Scan Synthess(Scan Confguraton) Scan Confguraton The number of scan chans used lmted by, share wth pns, can t put any on I/O pads, also lmted by on tester The types of scan cells used to mplement these scan chans pcked from Whch storage elements to exclude from the process or reasons How the scan cells are arranged consder and of scan cells SC SC 2 X Y X D D 2 D 3 Y D D 2 D 3 Mxng Fallng and Rsng Edges Page 48 24
25 27 Scan Desgn Flow Scan Synthess(Scan Confguraton) Crossng Clock Domans Lock up latch, or, put n to guarantee that any between the clocks can be Clock skew s not across Ths scheme works only when the between the clocks s less than the of the clock pulse Clock doman Clock doman 2 SC p Lock-up latch SC q D X Y Z 2 2 X D D 2 D 3 Y D D 2 D 3 Z D D 2 D 3 Page RTL Desgn for Testablty RTL desgn Logc synthess Gate-level desgn Testablty repar Testable desgn Scan synthess Scan desgn Gate-Level Testablty Repar Desgn Flow RTL desgn Testablty repar Testable RTL desgn Logc/scan synthess Scan desgn RTL-Level Testablty Repar Desgn Flow Page 5 25
26 29 RTL Desgn for Testablty RTL Scan Desgn and Repar(Example) clk) f (q == 4'b) clk_5 <= ; else begn clk_5 <= ; q <= q + ; end clk_5) d < = start; clk clk_5 start D d RTL Verlog Descrpton Generated Schematc Page 5 29 RTL Desgn for Testablty RTL Scan Desgn and Repar(Example) clk) f (q == 4'b) clk_5 <= ; else begn clk_5 <= ; q <= q + ; end assgn clk_test = (TM)? clk : clk_5; clk_test) d < = start; clk clk_5 start D clk_test TM d Repared RTL Verlog Descrpton Repared Schematc Page 52 26
27 2 Concludng Remarks DFT has become for ensurng product s the most wdely used technque New desgn and test challenges Further reduce test, test and test Cope wth falures of the desgn era Page 53 27
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 informationOutline. 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 informationCircuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)
Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,
More informationVRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,
VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual
More informationSimulation 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 informationConditional 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 informationRADIX-10 PARALLEL DECIMAL MULTIPLIER
RADIX-10 PARALLEL DECIMAL MULTIPLIER 1 MRUNALINI E. INGLE & 2 TEJASWINI PANSE 1&2 Electroncs Engneerng, Yeshwantrao Chavan College of Engneerng, Nagpur, Inda E-mal : mrunalngle@gmal.com, tejaswn.deshmukh@gmal.com
More informationThe stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0
The stream cpher MICKEY-128 (verson 1 Algorthm specfcaton ssue 1. Steve Babbage Vodafone Group R&D, Newbury, UK steve.babbage@vodafone.com Matthew Dodd Independent consultant matthew@mdodd.net www.mdodd.net
More informationCompiler 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 informationHermite 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 informationTEST GENERATION FOR SCAN DESIGN CIRCUITS WITH TRI-STATE MODULES AND BIDIRECTIONAL TERMINALS
TEST GENERATION FOR SCAN DESIGN CIRCUITS WITH TRI-STATE MODULES AND BIDIRECTIONAL TERMINALS Takuj Oghara ~k Shnch Mura Yuzo Takamatsu ~>k Kozo Knoshta ~ and Hdeo Fujwara ~ ~ ~k MtsubshJ Electrc Corp.,
More informationTHEORETICAL BACKGROUND FOR THE APPLET DESIGN AND TEST OF DIGITAL SYSTEMS ON RT-LEVEL AND RELATED EXERCISES
TALLINN TECHNICAL UNIVERSITY Faculty of Informaton Technology Department of Computer Engneerng Char of Computer Engneerng and Dagnostcs THEORETICAL BACKGROUND FOR THE APPLET DESIGN AND TEST OF DIGITAL
More informationThe 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 informationLife Tables (Times) Summary. Sample StatFolio: lifetable times.sgp
Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables
More informationLecture 3: Computer Arithmetic: Multiplication and Division
8-447 Lecture 3: Computer Arthmetc: Multplcaton and Dvson James C. Hoe Dept of ECE, CMU January 26, 29 S 9 L3- Announcements: Handout survey due Lab partner?? Read P&H Ch 3 Read IEEE 754-985 Handouts:
More informationHigh level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization
What s a Computer Program? Descrpton of algorthms and data structures to acheve a specfc ojectve Could e done n any language, even a natural language lke Englsh Programmng language: A Standard notaton
More informationWishing 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 informationDigital VLSI Testing Prof. Santanu Chattopadhyay Department of Electronics and EC Engineering India Institute of Technology, Kharagpur.
Digital VLSI Testing Prof. Santanu Chattopadhyay Department of Electronics and EC Engineering India Institute of Technology, Kharagpur Lecture 05 DFT Next we will look into the topic design for testability,
More informationTN348: 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 informationVirtual 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 informationDesign for Testability
Design for Testability Sungho Kang Yonsei University Outline Introduction Testability Measure Design for Testability Ad-Hoc Testable Design Conclusion 2 Merging Design and Test Design and Test become closer
More informationAssembler. Building a Modern Computer From First Principles.
Assembler Buldng a Modern Computer From Frst Prncples www.nand2tetrs.org Elements of Computng Systems, Nsan & Schocken, MIT Press, www.nand2tetrs.org, Chapter 6: Assembler slde Where we are at: Human Thought
More informationREMOTE SENSING REQUIREMENTS DEVELOPMENT: A SIMULATION-BASED APPROACH
REMOTE SENSING REQUIREMENTS DEVEOPMENT: A SIMUATION-BASED APPROAC V. Zanon a, B. Davs a, R. Ryan b, G. Gasser c, S. Blonsk b a Earth Scence Applcatons Drectorate, Natonal Aeronautcs and Space Admnstraton,
More informationImproved Symoblic Simulation By Dynamic Funtional Space Partitioning
Improved Symoblc Smulaton By Dynamc Funtonal Space Parttonng Tao Feng, L-.Wang, Kwang-Tng heng Department of EE, U-Santa Barbara, U.S.A tfeng,lcwang, tmcheng @ece.ucsb.edu Andy -. Ln adence Desgn Systems,
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationFunctional and Timing Validation of Partially Bypassed Processor Pipelines
Functonal and Tmng Valdaton of Partally Bypassed Processor Ppelnes Qang Zhu shyu@labs.fujtsu.com Avral Shrvastava Avral.Shrvastava@asu.edu Nl Dutt dutt@cs.uc.edu Fujtsu Laboratores LTD., Japan 1-1, Kamodanaa
More informationHow 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 informationSupport 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 informationAssignment # 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 informationA Power Optimization Toolbox for Logic Synthesis and Mapping
A Power Optmzaton Toolbox for Logc Synthess and Mappng Alan Mshchenko Robert Brayton Stephen Jang Kevn Chung Department of EECS, Unversty of Calforna, Berkeley LogcMll Technology Qualcom Innovatons {alanm,
More informationDesign of a High-Speed Asynchronous Turbo Decoder
Desgn of a Hgh-Speed Asynchronous Turbo Decoder Pana Golan Georgos D. Dmou, Malla Praash, Peter A. Beerel Department of Electrcal Engneerng-Systems Unversty of Southern Calforna, Los Angeles, CA 90089
More informationCHAPTER 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 informationEvaluation of an Enhanced Scheme for High-level Nested Network Mobility
IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.15 No.10, October 2015 1 Evaluaton of an Enhanced Scheme for Hgh-level Nested Network Moblty Mohammed Babker Al Mohammed, Asha Hassan.
More informationParallel 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 informationKent 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 informationAssembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface.
IDC Herzlya Shmon Schocken Assembler Shmon Schocken Sprng 2005 Elements of Computng Systems 1 Assembler (Ch. 6) Where we are at: Human Thought Abstract desgn Chapters 9, 12 abstract nterface H.L. Language
More informationA 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 informationA fault tree analysis strategy using binary decision diagrams
Loughborough Unversty Insttutonal Repostory A fault tree analyss strategy usng bnary decson dagrams Ths tem was submtted to Loughborough Unversty's Insttutonal Repostory by the/an author. Addtonal Informaton:
More informationHarvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6)
Harvard Unversty CS 101 Fall 2005, Shmon Schocken Assembler Elements of Computng Systems 1 Assembler (Ch. 6) Why care about assemblers? Because Assemblers employ some nfty trcks Assemblers are the frst
More informationConcurrent Apriori Data Mining Algorithms
Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng
More informationSimulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010
Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement
More informationStorage Binding in RTL synthesis
Storage Bndng n RTL synthess Pe Zhang Danel D. Gajsk Techncal Report ICS-0-37 August 0th, 200 Center for Embedded Computer Systems Department of Informaton and Computer Scence Unersty of Calforna, Irne
More informationFIBARO WALL PLUG OPERATING MANUAL FGBWHWPE-102/FGBWHWPF-102 CONTENTS
OPERATING MANUAL EN FIBARO WALL PLUG FGBWHWPE-102/FGBWHWPF-102 CONTENTS #1: Descrpton and features 3 #2: Parng the accessory 4 #3: Reset 5 #4: Functonalty 6 v1.0 #5: W-F 8 #6: Confgurable parameters 9
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationAn 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 informationMallathahally, Bangalore, India 1 2
7 IMPLEMENTATION OF HIGH PERFORMANCE BINARY SQUARER PRADEEP M C, RAMESH S, Department of Electroncs and Communcaton Engneerng, Dr. Ambedkar Insttute of Technology, Mallathahally, Bangalore, Inda pradeepmc@gmal.com,
More informationAgenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals
Agenda & Readng COMPSCI 8 SC Applcatons Programmng Programmng Fundamentals Control Flow Agenda: Decsonmakng statements: Smple If, Ifelse, nested felse, Select Case s Whle, DoWhle/Untl, For, For Each, Nested
More informationHigh-Level Power Modeling of CPLDs and FPGAs
Hgh-Level Power Modelng of CPLs and FPGAs L Shang and Nraj K. Jha epartment of Electrcal Engneerng Prnceton Unversty {lshang, jha}@ee.prnceton.edu Abstract In ths paper, we present a hgh-level power modelng
More informationParallel Inverse Halftoning by Look-Up Table (LUT) Partitioning
Parallel Inverse Halftonng by Look-Up Table (LUT) Parttonng Umar F. Sddq and Sadq M. Sat umar@ccse.kfupm.edu.sa, sadq@kfupm.edu.sa KFUPM Box: Department of Computer Engneerng, Kng Fahd Unversty of Petroleum
More informationUsing Delayed Addition Techniques to Accelerate Integer and Floating-Point Calculations in Configurable Hardware
Draft submtted for publcaton. Please do not dstrbute Usng Delayed Addton echnques to Accelerate Integer and Floatng-Pont Calculatons n Confgurable Hardware Zhen Luo, Nonmember and Margaret Martonos, Member,
More informationFPGA Implementation of CORDIC Algorithms for Sine and Cosine Generator
The 5th Internatonal Conference on Electrcal Engneerng and Informatcs 25 August -, 25, Bal, Indonesa FPGA Implementaton of CORDIC Algorthms for Sne and Cosne Generator Antonus P. Renardy, Nur Ahmad, Ashbr
More informationArea Efficient Self Timed Adders For Low Power Applications in VLSI
ISSN(Onlne): 2319-8753 ISSN (Prnt) :2347-6710 Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 3297: 2007 Certfed Organzaton) Area Effcent Self Tmed Adders For Low
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationMODELING THE RELIABILITY OF INFORMATION MANAGEMENT SYSTEMS BASED ON MISSION SPECIFIC TOOLS SET SOFTWARE
Knowledge Dynamcs MODELING THE ELIABILITY OF INFOMATION MANAGEMENT SYSTEMS BASED ON MISSION SPECIFIC TOOLS SET SOFTWAE Cezar VASILESCU Assocate Professor, egonal Department of Defense esources Management
More informationPetri Net Based Software Dependability Engineering
Proc. RELECTRONIC 95, Budapest, pp. 181-186; October 1995 Petr Net Based Software Dependablty Engneerng Monka Hener Brandenburg Unversty of Technology Cottbus Computer Scence Insttute Postbox 101344 D-03013
More informationA RECONFIGURABLE ARCHITECTURE FOR MULTI-GIGABIT SPEED CONTENT-BASED ROUTING. James Moscola, Young H. Cho, John W. Lockwood
A RECONFIGURABLE ARCHITECTURE FOR MULTI-GIGABIT SPEED CONTENT-BASED ROUTING James Moscola, Young H. Cho, John W. Lockwood Dept. of Computer Scence and Engneerng Washngton Unversty, St. Lous, MO {jmm5,
More information11. HARMS How To: CSV Import
and Rsk System 11. How To: CSV Import Preparng the spreadsheet for CSV Import Refer to the spreadsheet template to ad algnng spreadsheet columns wth Data Felds. The spreadsheet s shown n the Appendx, an
More informationMemory Modeling in ESL-RTL Equivalence Checking
11.4 Memory Modelng n ESL-RTL Equvalence Checkng Alfred Koelbl 2025 NW Cornelus Pass Rd. Hllsboro, OR 97124 koelbl@synopsys.com Jerry R. Burch 2025 NW Cornelus Pass Rd. Hllsboro, OR 97124 burch@synopsys.com
More information[33]. As we have seen there are different algorithms for compressing the speech. The
49 5. LD-CELP SPEECH CODER 5.1 INTRODUCTION Speech compresson s one of the mportant doman n dgtal communcaton [33]. As we have seen there are dfferent algorthms for compressng the speech. The mportant
More informationConcurrent models of computation for embedded software
Concurrent models of computaton for embedded software and hardware! Researcher overvew what t looks lke semantcs what t means and how t relates desgnng an actor language actor propertes and how to represent
More informationAnalysis of Min Sum Iterative Decoder using Buffer Insertion
Analyss of Mn Sum Iteratve ecoder usng Buffer Inserton Saravanan Swapna M.E II year, ept of ECE SSN College of Engneerng M. Anbuselv Assstant Professor, ept of ECE SSN College of Engneerng S.Salvahanan
More informationSorting 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 informationASSERTION SUPPORT IN HIGH-LEVEL SYNTHESIS DESIGN FLOW. 351 crs de la Libération, Talence, France
SSERTION SUPPORT IN HIGH-LEVEL SYNTHESIS DESIGN FLOW urélen Rbon, Bertrand Le Gal, Chrstophe Jégo and Domnque Dallet Unversté de Bordeaux, Laboratore IMS, CNRS UMR 528, IPB, 35 crs de la Lbératon, 33405
More informationCS 268: Lecture 8 Router Support for Congestion Control
CS 268: Lecture 8 Router Support for Congeston Control Ion Stoca Computer Scence Dvson Department of Electrcal Engneerng and Computer Scences Unversty of Calforna, Berkeley Berkeley, CA 9472-1776 Router
More informationClassifying Acoustic Transient Signals Using Artificial Intelligence
Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)
More informationCategories and Subject Descriptors B.7.2 [Integrated Circuits]: Design Aids Verification. General Terms Algorithms
3. Fndng Determnstc Soluton from Underdetermned Equaton: Large-Scale Performance Modelng by Least Angle Regresson Xn L ECE Department, Carnege Mellon Unversty Forbs Avenue, Pttsburgh, PA 3 xnl@ece.cmu.edu
More informationCMPS 10 Introduction to Computer Science Lecture Notes
CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not
More informationType-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data
Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationParallelism 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 informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationAnalysis of Non-coherent Fault Trees Using Ternary Decision Diagrams
Analyss of Non-coherent Fault Trees Usng Ternary Decson Dagrams Rasa Remenyte-Prescott Dep. of Aeronautcal and Automotve Engneerng Loughborough Unversty, Loughborough, LE11 3TU, England R.Remenyte-Prescott@lboro.ac.uk
More informationAn Associative Processor Array Designed for Computer Vision
An Assocatve Processor Array Desgned for Computer Vson A.W.G. Duller R.H. Storer A.R. Thomson M.R. Pout E.L. Dagless Dept. of Electrcal and Electronc Engneerng Unversty of Brstol Brstol BS8 1TR, UK. The
More informationGeneration of Compact Single-Detect Stuck-At Test Sets Targeting Unmodeled Defects
Generaton of Compact Sngle-Detect Stuck-At Test Sets Targetng Unmodeled Defects Xrysovalants Kavousanos and Krshnendu Chakrabarty * Abstract- We present a new method to generate compact stuckat test sets
More informationMeta-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 informationSystem-on-Chip Design Analysis of Control Data Flow. Hao Zheng Comp Sci & Eng U of South Florida
System-on-Chp Desgn Analyss of Control Data Flow Hao Zheng Comp Sc & Eng U of South Florda Overvew DF models descrbe concurrent computa=on at a very hgh level Each actor descrbes non-trval computa=on.
More informationPrivate Information Retrieval (PIR)
2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market
More informationOptimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming
Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered
More informationHelsinki 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 informationThe 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 informationVirtual 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 informationHierarchical 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 informationResearch and Application of Fingerprint Recognition Based on MATLAB
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Automaton and Control Systems Journal, 205, 7, 07-07 Open Access Research and Applcaton of Fngerprnt Recognton Based on MATLAB Nng Lu* Department
More informationA Flexible Architecture for Creating Scheduling Algorithms as used in STK Scheduler
A Flexble Archtecture for Creatng Schedulng Algorthms as used n STK Scheduler W. A. Fsher and Ella Herz Optwse Corporaton and Orbt Logc Incorporated fsher@optwse.com and ella.herz@orbtlogc.com Abstract
More informationAn 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 informationSpecifications in 2001
Specfcatons n 200 MISTY (updated : May 3, 2002) September 27, 200 Mtsubsh Electrc Corporaton Block Cpher Algorthm MISTY Ths document shows a complete descrpton of encrypton algorthm MISTY, whch are secret-key
More informationEfficient Distributed File System (EDFS)
Effcent Dstrbuted Fle System (EDFS) (Sem-Centralzed) Debessay(Debsh) Fesehaye, Rahul Malk & Klara Naherstedt Unversty of Illnos-Urbana Champagn Contents Problem Statement, Related Work, EDFS Desgn Rate
More informationOn Reconfiguration-Oriented Approximate Adder Design and Its Application
On Reconfguraton-Orented Approxmate Adder Desgn and Its Applcaton Rong Ye, Tng Wang, Feng Yuan, Rakesh Kumar and Qang Xu Chk RElable Computng Laboratory (CRE) Department of Computer Scence & Engneerng
More informationTolerating Transient Faults in Statically Scheduled Safety-Critical Embedded Systems
Toleratng Transent Faults n Statcally Scheduled Safety-Crtcal Embedded Systems Nagaraan Kandasamy *, John P. Hayes *, and Bran T. Murray ** * Department of Electrcal Engneerng ** Delph Automotve Systems
More informationSum 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 informationDesign and Analysis of Algorithms
Desgn and Analyss of Algorthms Heaps and Heapsort Reference: CLRS Chapter 6 Topcs: Heaps Heapsort Prorty queue Huo Hongwe Recap and overvew The story so far... Inserton sort runnng tme of Θ(n 2 ); sorts
More informationSensor-aware Adaptive Pull-Push Query Processing for Sensor Networks
Sensor-aware Adaptve Pull-Push Query Processng for Sensor Networks Raja Bose Unversty of Florda Ganesvlle, FL 326 U.S.A. rbose@cse.ufl.edu Abdelsalam Helal Unversty of Florda Ganesvlle, FL 326 U.S.A. helal@cse.ufl.edu
More informationAADL : about scheduling analysis
AADL : about schedulng analyss Schedulng analyss, what s t? Embedded real-tme crtcal systems have temporal constrants to meet (e.g. deadlne). Many systems are bult wth operatng systems provdng multtaskng
More informationETAtouch RESTful Webservices
ETAtouch RESTful Webservces Verson 1.1 November 8, 2012 Contents 1 Introducton 3 2 The resource /user/ap 6 2.1 HTTP GET................................... 6 2.2 HTTP POST..................................
More informationPerformance analysis of distributed cluster-based MAC protocol for multiuser MIMO wireless networks
RESEARCH Open Access Performance analyss of dstrbuted cluster-based MAC protocol for multuser MIMO wreless networks Azadeh Ettefagh *, Marc Kuhn, Celal Eşl and Armn Wttneben Abstract It s known that multuser
More informationModel Integrated Computing: A Framework for Creating Domain Specific Design Environments
Model Integrated Computng: A Framework for Creatng Doman Specfc Desgn Envronments James R. DAVIS Vanderblt Unversty, Insttute for Software Integrated Systems Nashvlle, TN 37203, USA ABSTRACT Model Integrated
More informationSetup and Use. Version 3.7 2/1/2014
Verson 3.7 2/1/2014 Setup and Use MaestroSoft, Inc. 1750 112th Avenue NE, Sute A200, Bellevue, WA 98004 425.688.0809 / 800.438.6498 Fax: 425.688.0999 www.maestrosoft.com Contents Text2Bd checklst 3 Preparng
More informationProblem Set 3 Solutions
Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,
More informationA Saturation Binary Neural Network for Crossbar Switching Problem
A Saturaton Bnary Neural Network for Crossbar Swtchng Problem Cu Zhang 1, L-Qng Zhao 2, and Rong-Long Wang 2 1 Department of Autocontrol, Laonng Insttute of Scence and Technology, Benx, Chna bxlkyzhangcu@163.com
More informationTECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS. Muradaliyev A.Z.
TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS Muradalyev AZ Azerbajan Scentfc-Research and Desgn-Prospectng Insttute of Energetc AZ1012, Ave HZardab-94 E-mal:aydn_murad@yahoocom Importance of
More information