Outline Last time: Deriving the State Diagram & Datapath (Cont.) Mapping the Datapath onto Control

Size: px
Start display at page:

Download "Outline Last time: Deriving the State Diagram & Datapath (Cont.) Mapping the Datapath onto Control"

Transcription

1 Outline Lst time: Deriving the Stte Digrm & Dtpth (Cont.) Mpping the Dtpth onto Control This lecture: Comintionl Testility nd Test-pttern Genertion Fults in digitl circuits Wht is test? : Controllility & Oservility Redundncy & testility Test coverge & simple PODEM ATPG Sequentil Test: Wht re sequentil fults? SCAN Design Outline Bckground: Role of Don't-Cres in Logic Synthesis Controllility & Oservility Optimlity, Redundncy & Testility The Sequentil Test Prolem Synthesis-Directed Sequentil Test Two Approches to Full Testility Effectiveness nd Limittions so fr.... Role of Don't-Cres in Logic Synthesis Role of Don't-Cres in Logic Synthesis Comintionl Logic f Comintionl Logic f f = cnnot e reduced further in isoltion cn never hppen Don't-Cre Set: D = ' ( + ) + ' ' Minimize f with respect to D Role of Don't-Cres in Logic Synthesis f = Fult Ecittion stuck-t

2 Fult Models Input or output pin (not entire net!) stuck t logic 0 or stuck t logic. Open circuit Cn mke comintionl circuit sequentil! Short circuit.. 0 Fult Propgtion stuck-t-0 The test is cue, not minterm...8 Optimlity & Redundncy in Comintionl Logic c d Circuit with redundnt fult: stuck-t-0 f = (.).(c+d).c' = (..c +..d).c' =..c.c' +..d.c' =..d.c' f c d Circuit with = 0 f = (.).c'.d f Pth-Oriented DEcision Mking [Goel, 98] () Assign ll (PI) to the vlue "don't cre" ( ). () Given n output signl nd desired vlue for the output, trce pth to the PIs to otin PI ssignment. (3) Simulte the PI vector to see if it sets up the desired vlue on the output. If so, terminte. (4) If the opposite vlue is set, ssign n opposite vlue to the PI nd re-simulte. If desired vlue is set, terminte. (5) If the output remins unspecified, repet the pth trcing to set nother PI, s necessry. Procedure continues until either: A successful PI ssignment hs een found (circuits not equivlent). All possile PI ssignments hve een ehusted c d Cover Etrction w ON cd OFF cd 0 0 d= d=0 c= c=0 =0 = =0 = Covers cn e generted with s mny "don't cres" in the present stte prt s possile. Testility nd Logic Synthesis Importnt Issue: Generting tests for circuits with redundncies is very difficult. Must use lgorithms which decrese the numer of redundncies or eliminte them completely during synthesis.....

3 Test Genertion for Finite-Stte Mchines Irredundnt comintionl logic does not imply 00% sequentil testility Sequentil Fults: Fults my not e ecited ("controlled") y primry inputs; fults my not e propgted to primry outputs ("oserved"). (PIs) Finite-Stte Mchines Comintionl Net-Stte Logic Ltches Comintionl Output Logic (POs) Moore Mchine (PIs) Finite-Stte Mchines Comintionl Net-Stte Logic Ltches (POs) Mely Mchine Emple Finite-Stte Mchine: Stte Trnsition Digrm / A 0/ /0 0/0 0/ B /0 0/ /0 E / C D 0/ Emple Finite-Stte Mchine: Encoded Sttes / / /0 0/0 0/ 00 0/0 /0 0/ / / 0 0 Stte Assignment Find inry encoding of sttes which minimizes the eventul re (or dely) of the FSM fter comintionl logic optimiztion of nd OL. FSM ENCODE OPTIMIZE Need to predict nd model the optimiztion Stte ssignment hs mjor effect on testility.....8

4 Emple Finite-Stte Mchine: Net-Stte Logic Mely Mchine t Time tn in ps(3)' ps()' ps() ns() ns() ns(3) PIn L(Sn ) PO n ps()' out in' ps() Sn is stte of ltches t time tn Finite-Stte Mchine s Iterted Arry Idel Iterted Arry PI PO PI PO PI n PO n PI PO PI PO PI n PO n L(S 0 ) L(S ) L(S ) L(S n ) L(S 0 ) n L(S ) L(S ) L(S n ) Fult is present in ll copies of Fult my msk ecittion or propgtion More likely, fult my cuse net-stte N to e invlid stte Nf. In n idel sitution, the would e optimized seprtely for ech possile stte trnsition! Ech lock would e mde prime nd irredundnt seprtely..... PIs Sequentil Circuits: Controllility & Oservility Comintionl Net-Stte Logic Ltches POs A test: 00 : 00 : 00 0:0 : 0 0: Scn Design Mke ll flip-flops scn (i.e. direct red nd write ccess) All inputs to the comintionl logic cn e set nd ll outputs cn e red. The sequentil testing prolem ecomes comintionl testing prolem. "Overkill" in virtully ll cses. Are nd time penlty; often longer testing time. But scn cn e inserted utomticlly

5 Synthesis Procedure for Fully-Testle Non-Scn Finite-Stte Mchine (Devds, et.l. 988) (PIs) N Ltches Output Logic (POs) Prtition into single-cone circuits Single stuck-fult correct & incorrect net-stte differ y ectly one it. Perform stte ssignment such tht ll sttes differing in one it ssert different outputs one-step propgtion Synthesis Procedure for Fully-Testle Non-Scn Finite-Stte Mchines Given stte-trnsition grph (STG) of FSM, 00%-testle logic-level implementtion of the mchine is produced No scnnle ltches required Uses prtitioned logic pproch nd constrined stte ssignment Smll penlty Cscded Finite-Stte Mchines Coupled Finite-Stte Mchines PIs POs e.g. controller Is it possile to synthesize cscde of FSM's such tht ll emedded fults re detectle non-scn from the eternl inputs only? Wht is the penlty in rel cses? PIs e.g. dt pth POs....8 Nme sse tk plnet scf Emple FSMs Sttes Edges Nme sse tk plnet Constrined Stte Assignment (single cones) Fult Gtes Cover (%) Optimized only TPG time 0s s 04s 33s Optimized nd Testle Fult Gtes Cover (%) scf m Output logic lock contined comintionl redundncies TPG time 5s 4s s 4s s

6 Nme sse tk plnet scf Constrined Stte Assignment (single cones) Optimized only Normlized Gtes Are Optimized nd Testle Normlized Gtes Are Effect of Gte Dupliction in Stndrd-Cell Lyout logic gtes routing trcks Dupliction of gtes reduces routing congestion nd my sve routing trck. The re of routing trck is usully >> the re of gte. Reducing mimum gte fnout my improve performnce...3 Emple Finite-Stte Mchine with Fult d in ps(3)' ps()' ps() ps()' in' ps() stuck-t-0 ns() ns() ns(3) out Emple Finite-Stte Mchine Effect of Fult d / / /0 0/0 0/ 00 0/0 /0 0/ / / N f N, where N f is vlid stte Emple Finite-Stte Mchine with Fult g in ps(3)' ps()' ps() ps()' in' ps() γstuck-t- ns() ns() ns(3) out Emple Finite-Stte Mchine Effect of Fult g / / 0/0 0/ /0 0/ 00 /0 0/ 0/0 /0 / / N f N, where N f is invlid stte (stte splitting hs occurred)

7 Use of Etended Don't-Cre Set to Gurntee Testility (Devds & Keutzer, '90) Etrct the Stte Grph Anlyze the Stte Equivlences Generte the Etended Don't-Cre Set Informtion Optimize for Prime nd Irredundnt Network Under the Don't-Cre Set Chnged? Fully Testle Nme e e s key Emple FSMs 8 6 Sttes Edges Results of Synthesis Procedure Results Using Etended Don't-Cre Sets During Synthesis Nme e Ltches 3 Gtes 3 Fult Cover (%) 9.9 OptimizeTPGidentify remove redund. redund. 0.5s.0s.s.0s Nme e Stte Enum. 0.5s Optimize Time 0.5s TPG.s Logic Optimize Fult Cover (%) 00.0 e s 4s 6.s.8m e 6.5s.4s 4s 00.0 s s 303s 4.0s 303s s.0s 6.s 98s s 33s 4s >h 0.s 5.5s 4s key s m.m >h key 4.6s.8s m 00.0 %-5% smller designs thn without don't-cre sets Test Procedure: Scn Test Procedure: Non-Scn Npi Npo Npi Npo Nl Nl+Npi input its per test One tester clock tick per it Nl Npi input its per test One tester clock tick per Npi its..4..4

8 Wht Aout Testing Time? (Ghosh et. l. 989) Numer of Test Bits Nme Scn S for T e 4,03 3,8 e 9,696,50 e3 55,680 34,90 des 868,86 key,856 5,968 viteri 5,68 4,950 Viteri Chip Prt of system for rel-time continuous speech recognition developed y Prof's. Broderson & Rey t Berkeley. Lrgest chip in the chip-set for the system. Implements the Viteri lgorithm for mpping n oservtion (some speech) into the most likely sequence of sttes in the speech model eing used. Chip Sttistics: 5,000 trnsistors 6 inputs, 44 outputs. 0.5 mm die size Wht Aout Testing Time? A Revolution in Test in the Lte 990s? Tester Cycles Test Bits Nme Scn S for T Scn S for T e 4, ,03 3,8 e 9, ,696,50 e3 55,680,033 55,680 34,90 des 8, ,86 key,856 03,856 5,968 viteri 5,68,045 5,68 4,950 Cn Synthesize Gurnteed Fully-Testle, Non-Scn Implementtion of Any Collection of FSMs. Almost lwys requires fewer gtes or less re thn full scn. Almost lwys requires shorter tester times (in mny cses y one or two orders of mgnitude) thn full scn. Cn hndle fults in emedded mchines, mchines with feedck, etc. - ny topology of interconnected mchines. Test ptterns generted s y-product of the synthesis, so synthesis time represents sving of ATPG time Synthesis-Directed Sequentil Test Entire-chip full-scn-sed design-for-test will e osolete y the end of the 990s Will e used for some very-specific on-chip structures (e.g. ROM, RAM, mye Dtpth) nd for some chip oundries. Circuit-structure-specific nd BILBO-like test styles will continue to e used for go-nogo tests. Architecturl memory structures will continue to e ccessile directly for the pins. Synthesis-Directed Sequentil Test Test will e incorported directly into the synthesis process Gurnteed fully-testle non-scn or prtilscn designs will e produced y the synthesis process. A complete set of test ptterns will e yproduct of the process

Fault Modeling. EE5375 ADD II Prof. MacDonald

Fault Modeling. EE5375 ADD II Prof. MacDonald Fult Modeling EE5375 ADD II Prof. McDonld Stuck At Fult Models l Modeling of physicl defects (fults) simplify to logicl fult l stuck high or low represents mny physicl defects esy to simulte technology

More information

EE273 Lecture 15 Asynchronous Design November 16, Today s Assignment

EE273 Lecture 15 Asynchronous Design November 16, Today s Assignment EE273 Lecture 15 Asynchronous Design Novemer 16, 199 Willim J. Dlly Computer Systems Lortory Stnford University illd@csl.stnford.edu 1 Tody s Assignment Term Project see project updte hndout on we checkpoint

More information

Convert the NFA into DFA

Convert the NFA into DFA Convert the NF into F For ech NF we cn find F ccepting the sme lnguge. The numer of sttes of the F could e exponentil in the numer of sttes of the NF, ut in prctice this worst cse occurs rrely. lgorithm:

More information

Resources. Introduction: Binding. Resource Types. Resource Sharing. The type of a resource denotes its ability to perform different operations

Resources. Introduction: Binding. Resource Types. Resource Sharing. The type of a resource denotes its ability to perform different operations Introduction: Binding Prt of 4-lecture introduction Scheduling Resource inding Are nd performnce estimtion Control unit synthesis This lecture covers Resources nd resource types Resource shring nd inding

More information

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true. York University CSE 2 Unit 3. DFA Clsses Converting etween DFA, NFA, Regulr Expressions, nd Extended Regulr Expressions Instructor: Jeff Edmonds Don t chet y looking t these nswers premturely.. For ech

More information

6.004 Computation Structures Spring 2009

6.004 Computation Structures Spring 2009 MIT OpenCourseWre http://ocw.mit.edu 6.004 Computtion Structures Spring 009 For informtion out citing these mterils or our Terms of Use, visit: http://ocw.mit.edu/terms. Cost/Performnce Trdeoffs: cse study

More information

First Midterm Examination

First Midterm Examination 24-25 Fll Semester First Midterm Exmintion ) Give the stte digrm of DFA tht recognizes the lnguge A over lphet Σ = {, } where A = {w w contins or } 2) The following DFA recognizes the lnguge B over lphet

More information

expression simply by forming an OR of the ANDs of all input variables for which the output is

expression simply by forming an OR of the ANDs of all input variables for which the output is 2.4 Logic Minimiztion nd Krnugh Mps As we found ove, given truth tle, it is lwys possile to write down correct logic expression simply y forming n OR of the ANDs of ll input vriles for which the output

More information

Digital Control of Electric Drives

Digital Control of Electric Drives igitl Control o Electric rives Logic Circuits - Comintionl Boolen Alger, escription Form Czech Technicl University in Prgue Fculty o Electricl Engineering Ver.. J. Zdenek Logic Comintionl Circuit Logic

More information

Minimal DFA. minimal DFA for L starting from any other

Minimal DFA. minimal DFA for L starting from any other Miniml DFA Among the mny DFAs ccepting the sme regulr lnguge L, there is exctly one (up to renming of sttes) which hs the smllest possile numer of sttes. Moreover, it is possile to otin tht miniml DFA

More information

Efficient Implication - Based Untestable Bridge Fault Identifier*

Efficient Implication - Based Untestable Bridge Fault Identifier* Efficient Impliction - Bsed Untestle Bridge Fult Identifier* Mnn Syl, Michel S. Hsio, Kirn B. Doreswmy nd Sreejit Chkrvrty Brdley Deprtment of Electricl nd Computer Engineering, Virgini Tech, Blcksurg,

More information

Assignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages

Assignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages Deprtment of Computer Science, Austrlin Ntionl University COMP2600 Forml Methods for Softwre Engineering Semester 2, 206 Assignment Automt, Lnguges, nd Computility Smple Solutions Finite Stte Automt nd

More information

ECE 327 Solution to Midterm 2016t1 (Winter)

ECE 327 Solution to Midterm 2016t1 (Winter) ECE 7 Solution to Midterm 6t (Winter) All requests for re-mrks must be submitted in writing to Mrk Agrd before 8:m on ridy Mrch. A rndom collection of midterms were scnned. Exms tht re submitted for re-mrking

More information

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University CS415 Compilers Lexicl Anlysis nd These slides re sed on slides copyrighted y Keith Cooper, Ken Kennedy & Lind Torczon t Rice University First Progrmming Project Instruction Scheduling Project hs een posted

More information

State Minimization for DFAs

State Minimization for DFAs Stte Minimiztion for DFAs Red K & S 2.7 Do Homework 10. Consider: Stte Minimiztion 4 5 Is this miniml mchine? Step (1): Get rid of unrechle sttes. Stte Minimiztion 6, Stte is unrechle. Step (2): Get rid

More information

CS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.)

CS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.) CS 373, Spring 29. Solutions to Mock midterm (sed on first midterm in CS 273, Fll 28.) Prolem : Short nswer (8 points) The nswers to these prolems should e short nd not complicted. () If n NF M ccepts

More information

CS 188: Artificial Intelligence Spring 2007

CS 188: Artificial Intelligence Spring 2007 CS 188: Artificil Intelligence Spring 2007 Lecture 3: Queue-Bsed Serch 1/23/2007 Srini Nrynn UC Berkeley Mny slides over the course dpted from Dn Klein, Sturt Russell or Andrew Moore Announcements Assignment

More information

Fast Frequent Free Tree Mining in Graph Databases

Fast Frequent Free Tree Mining in Graph Databases The Chinese University of Hong Kong Fst Frequent Free Tree Mining in Grph Dtses Peixing Zho Jeffrey Xu Yu The Chinese University of Hong Kong Decemer 18 th, 2006 ICDM Workshop MCD06 Synopsis Introduction

More information

CS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation

CS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation CS2N: The Coming Revolution in Computer Architecture Lortory 2 Preprtion Ojectives:. Understnd the principle of sttic CMOS gte circuits 2. Build simple logic gtes from MOS trnsistors 3. Evlute these gtes

More information

Math 131. Numerical Integration Larson Section 4.6

Math 131. Numerical Integration Larson Section 4.6 Mth. Numericl Integrtion Lrson Section. This section looks t couple of methods for pproimting definite integrls numericlly. The gol is to get good pproimtion of the definite integrl in problems where n

More information

Bases for Vector Spaces

Bases for Vector Spaces Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything

More information

Combinational Logic. Precedence. Quick Quiz 25/9/12. Schematics à Boolean Expression. 3 Representations of Logic Functions. Dr. Hayden So.

Combinational Logic. Precedence. Quick Quiz 25/9/12. Schematics à Boolean Expression. 3 Representations of Logic Functions. Dr. Hayden So. 5/9/ Comintionl Logic ENGG05 st Semester, 0 Dr. Hyden So Representtions of Logic Functions Recll tht ny complex logic function cn e expressed in wys: Truth Tle, Boolen Expression, Schemtics Only Truth

More information

GNFA GNFA GNFA GNFA GNFA

GNFA GNFA GNFA GNFA GNFA DFA RE NFA DFA -NFA REX GNFA Definition GNFA A generlize noneterministic finite utomton (GNFA) is grph whose eges re lele y regulr expressions, with unique strt stte with in-egree, n unique finl stte with

More information

Centrum voor Wiskunde en Informatica REPORTRAPPORT. Supervisory control for nondeterministic systems

Centrum voor Wiskunde en Informatica REPORTRAPPORT. Supervisory control for nondeterministic systems Centrum voor Wiskunde en Informtic REPORTRAPPORT Supervisory control for nondeterministic systems A. Overkmp Deprtment of Opertions Reserch, Sttistics, nd System Theory BS-R9411 1994 Supervisory Control

More information

First Midterm Examination

First Midterm Examination Çnky University Deprtment of Computer Engineering 203-204 Fll Semester First Midterm Exmintion ) Design DFA for ll strings over the lphet Σ = {,, c} in which there is no, no nd no cc. 2) Wht lnguge does

More information

ECE223. R eouven Elbaz Office room: DC3576

ECE223. R eouven Elbaz Office room: DC3576 ECE223 R eouven Elz reouven@uwterloo.c Office room: DC3576 Outline Decoders Decoders with Enle VHDL Exmple Multiplexers Multiplexers with Enle VHDL Exmple From Decoder to Multiplexer 3-stte Gtes Multiplexers

More information

y1 y2 DEMUX a b x1 x2 x3 x4 NETWORK s1 s2 z1 z2

y1 y2 DEMUX a b x1 x2 x3 x4 NETWORK s1 s2 z1 z2 BOOLEAN METHODS Giovnni De Miheli Stnford University Boolen methods Exploit Boolen properties. { Don't re onditions. Minimiztion of the lol funtions. Slower lgorithms, etter qulity results. Externl don't

More information

Lexical Analysis Finite Automate

Lexical Analysis Finite Automate Lexicl Anlysis Finite Automte CMPSC 470 Lecture 04 Topics: Deterministic Finite Automt (DFA) Nondeterministic Finite Automt (NFA) Regulr Expression NFA DFA A. Finite Automt (FA) FA re grph, like trnsition

More information

Chapter 2 Finite Automata

Chapter 2 Finite Automata Chpter 2 Finite Automt 28 2.1 Introduction Finite utomt: first model of the notion of effective procedure. (They lso hve mny other pplictions). The concept of finite utomton cn e derived y exmining wht

More information

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true. York University CSE 2 Unit 3. DFA Clsses Converting etween DFA, NFA, Regulr Expressions, nd Extended Regulr Expressions Instructor: Jeff Edmonds Don t chet y looking t these nswers premturely.. For ech

More information

Jin-Fu Li. Department of Electrical Engineering National Central University Jhongli, Taiwan

Jin-Fu Li. Department of Electrical Engineering National Central University Jhongli, Taiwan Trnsprent BIST for RAMs Jin-Fu Li Advnced d Relible Systems (ARES) Lb. Deprtment of Electricl Engineering Ntionl Centrl University Jhongli, Tiwn Outline Introduction Concept of Trnsprent Test Trnsprent

More information

12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2014

12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2014 CS125 Lecture 12 Fll 2014 12.1 Nondeterminism The ide of nondeterministic computtions is to llow our lgorithms to mke guesses, nd only require tht they ccept when the guesses re correct. For exmple, simple

More information

Lecture 3. Introduction digital logic. Notes. Notes. Notes. Representations. February Bern University of Applied Sciences.

Lecture 3. Introduction digital logic. Notes. Notes. Notes. Representations. February Bern University of Applied Sciences. Lecture 3 Ferury 6 ern University of pplied ciences ev. f57fc 3. We hve seen tht circuit cn hve multiple (n) inputs, e.g.,, C, We hve lso seen tht circuit cn hve multiple (m) outputs, e.g. X, Y,, ; or

More information

Worked out examples Finite Automata

Worked out examples Finite Automata Worked out exmples Finite Automt Exmple Design Finite Stte Automton which reds inry string nd ccepts only those tht end with. Since we re in the topic of Non Deterministic Finite Automt (NFA), we will

More information

Learning Moore Machines from Input-Output Traces

Learning Moore Machines from Input-Output Traces Lerning Moore Mchines from Input-Output Trces Georgios Gintmidis 1 nd Stvros Tripkis 1,2 1 Alto University, Finlnd 2 UC Berkeley, USA Motivtion: lerning models from blck boxes Inputs? Lerner Forml Model

More information

The Trapezoidal Rule

The Trapezoidal Rule _.qd // : PM Pge 9 SECTION. Numericl Integrtion 9 f Section. The re of the region cn e pproimted using four trpezoids. Figure. = f( ) f( ) n The re of the first trpezoid is f f n. Figure. = Numericl Integrtion

More information

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below.

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below. Dulity #. Second itertion for HW problem Recll our LP emple problem we hve been working on, in equlity form, is given below.,,,, 8 m F which, when written in slightly different form, is 8 F Recll tht we

More information

p-adic Egyptian Fractions

p-adic Egyptian Fractions p-adic Egyptin Frctions Contents 1 Introduction 1 2 Trditionl Egyptin Frctions nd Greedy Algorithm 2 3 Set-up 3 4 p-greedy Algorithm 5 5 p-egyptin Trditionl 10 6 Conclusion 1 Introduction An Egyptin frction

More information

Signal Flow Graphs. Consider a complex 3-port microwave network, constructed of 5 simpler microwave devices:

Signal Flow Graphs. Consider a complex 3-port microwave network, constructed of 5 simpler microwave devices: 3/3/009 ignl Flow Grphs / ignl Flow Grphs Consider comple 3-port microwve network, constructed of 5 simpler microwve devices: 3 4 5 where n is the scttering mtri of ech device, nd is the overll scttering

More information

Connected-components. Summary of lecture 9. Algorithms and Data Structures Disjoint sets. Example: connected components in graphs

Connected-components. Summary of lecture 9. Algorithms and Data Structures Disjoint sets. Example: connected components in graphs Prm University, Mth. Deprtment Summry of lecture 9 Algorithms nd Dt Structures Disjoint sets Summry of this lecture: (CLR.1-3) Dt Structures for Disjoint sets: Union opertion Find opertion Mrco Pellegrini

More information

Formal languages, automata, and theory of computation

Formal languages, automata, and theory of computation Mälrdlen University TEN1 DVA337 2015 School of Innovtion, Design nd Engineering Forml lnguges, utomt, nd theory of computtion Thursdy, Novemer 5, 14:10-18:30 Techer: Dniel Hedin, phone 021-107052 The exm

More information

CMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014

CMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014 CMPSCI 250: Introduction to Computtion Lecture #31: Wht DFA s Cn nd Cn t Do Dvid Mix Brrington 9 April 2014 Wht DFA s Cn nd Cn t Do Deterministic Finite Automt Forml Definition of DFA s Exmples of DFA

More information

CSE : Exam 3-ANSWERS, Spring 2011 Time: 50 minutes

CSE : Exam 3-ANSWERS, Spring 2011 Time: 50 minutes CSE 260-002: Exm 3-ANSWERS, Spring 20 ime: 50 minutes Nme: his exm hs 4 pges nd 0 prolems totling 00 points. his exm is closed ook nd closed notes.. Wrshll s lgorithm for trnsitive closure computtion is

More information

Homework 3 Solutions

Homework 3 Solutions CS 341: Foundtions of Computer Science II Prof. Mrvin Nkym Homework 3 Solutions 1. Give NFAs with the specified numer of sttes recognizing ech of the following lnguges. In ll cses, the lphet is Σ = {,1}.

More information

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3 2 The Prllel Circuit Electric Circuits: Figure 2- elow show ttery nd multiple resistors rrnged in prllel. Ech resistor receives portion of the current from the ttery sed on its resistnce. The split is

More information

Designing finite automata II

Designing finite automata II Designing finite utomt II Prolem: Design DFA A such tht L(A) consists of ll strings of nd which re of length 3n, for n = 0, 1, 2, (1) Determine wht to rememer out the input string Assign stte to ech of

More information

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata CS103B ndout 18 Winter 2007 Ferury 28, 2007 Finite Automt Initil text y Mggie Johnson. Introduction Severl childrens gmes fit the following description: Pieces re set up on plying ord; dice re thrown or

More information

DFA minimisation using the Myhill-Nerode theorem

DFA minimisation using the Myhill-Nerode theorem DFA minimistion using the Myhill-Nerode theorem Johnn Högerg Lrs Lrsson Astrct The Myhill-Nerode theorem is n importnt chrcteristion of regulr lnguges, nd it lso hs mny prcticl implictions. In this chpter,

More information

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. NFA for (a b)*abb.

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. NFA for (a b)*abb. CMSC 330: Orgniztion of Progrmming Lnguges Finite Automt 2 Types of Finite Automt Deterministic Finite Automt () Exctly one sequence of steps for ech string All exmples so fr Nondeterministic Finite Automt

More information

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. Comparing DFAs and NFAs (cont.) Finite Automata 2

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. Comparing DFAs and NFAs (cont.) Finite Automata 2 CMSC 330: Orgniztion of Progrmming Lnguges Finite Automt 2 Types of Finite Automt Deterministic Finite Automt () Exctly one sequence of steps for ech string All exmples so fr Nondeterministic Finite Automt

More information

Chapter 4: Techniques of Circuit Analysis. Chapter 4: Techniques of Circuit Analysis

Chapter 4: Techniques of Circuit Analysis. Chapter 4: Techniques of Circuit Analysis Chpter 4: Techniques of Circuit Anlysis Terminology Node-Voltge Method Introduction Dependent Sources Specil Cses Mesh-Current Method Introduction Dependent Sources Specil Cses Comprison of Methods Source

More information

CMSC 330: Organization of Programming Languages

CMSC 330: Organization of Programming Languages CMSC 330: Orgniztion of Progrmming Lnguges Finite Automt 2 CMSC 330 1 Types of Finite Automt Deterministic Finite Automt (DFA) Exctly one sequence of steps for ech string All exmples so fr Nondeterministic

More information

Compiler Design. Fall Lexical Analysis. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Fall Lexical Analysis. Sample Exercises and Solutions. Prof. Pedro C. Diniz University of Southern Cliforni Computer Science Deprtment Compiler Design Fll Lexicl Anlysis Smple Exercises nd Solutions Prof. Pedro C. Diniz USC / Informtion Sciences Institute 4676 Admirlty Wy, Suite

More information

CSCI 340: Computational Models. Kleene s Theorem. Department of Computer Science

CSCI 340: Computational Models. Kleene s Theorem. Department of Computer Science CSCI 340: Computtionl Models Kleene s Theorem Chpter 7 Deprtment of Computer Science Unifiction In 1954, Kleene presented (nd proved) theorem which (in our version) sttes tht if lnguge cn e defined y ny

More information

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER LANGUAGES AND COMPUTATION ANSWERS

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER LANGUAGES AND COMPUTATION ANSWERS The University of Nottinghm SCHOOL OF COMPUTER SCIENCE LEVEL 2 MODULE, SPRING SEMESTER 2016 2017 LNGUGES ND COMPUTTION NSWERS Time llowed TWO hours Cndidtes my complete the front cover of their nswer ook

More information

CSCI 340: Computational Models. Transition Graphs. Department of Computer Science

CSCI 340: Computational Models. Transition Graphs. Department of Computer Science CSCI 340: Computtionl Models Trnsition Grphs Chpter 6 Deprtment of Computer Science Relxing Restrints on Inputs We cn uild n FA tht ccepts only the word! 5 sttes ecuse n FA cn only process one letter t

More information

Thomas Whitham Sixth Form

Thomas Whitham Sixth Form Thoms Whithm Sith Form Pure Mthemtics Unit C Alger Trigonometry Geometry Clculus Vectors Trigonometry Compound ngle formule sin sin cos cos Pge A B sin Acos B cos Asin B A B sin Acos B cos Asin B A B cos

More information

Reinforcement Learning

Reinforcement Learning Reinforcement Lerning Tom Mitchell, Mchine Lerning, chpter 13 Outline Introduction Comprison with inductive lerning Mrkov Decision Processes: the model Optiml policy: The tsk Q Lerning: Q function Algorithm

More information

Engr354: Digital Logic Circuits

Engr354: Digital Logic Circuits Engr354: Digitl Logi Ciruits Chpter 4: Logi Optimiztion Curtis Nelson Logi Optimiztion In hpter 4 you will lern out: Synthesis of logi funtions; Anlysis of logi iruits; Tehniques for deriving minimum-ost

More information

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!!

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!! Nme: Algebr II Honors Pre-Chpter Homework Before we cn begin Ch on Rdicls, we need to be fmilir with perfect squres, cubes, etc Try nd do s mny s you cn without clcultor!!! n The nth root of n n Be ble

More information

Lecture 6. Notes. Notes. Notes. Representations Z A B and A B R. BTE Electronics Fundamentals August Bern University of Applied Sciences

Lecture 6. Notes. Notes. Notes. Representations Z A B and A B R. BTE Electronics Fundamentals August Bern University of Applied Sciences Lecture 6 epresenttions epresenttions TE52 - Electronics Fundmentls ugust 24 ern University of pplied ciences ev. c2d5c88 6. Integers () sign-nd-mgnitude representtion The set of integers contins the Nturl

More information

Thoery of Automata CS402

Thoery of Automata CS402 Thoery of Automt C402 Theory of Automt Tle of contents: Lecture N0. 1... 4 ummry... 4 Wht does utomt men?... 4 Introduction to lnguges... 4 Alphets... 4 trings... 4 Defining Lnguges... 5 Lecture N0. 2...

More information

Table of contents: Lecture N Summary... 3 What does automata mean?... 3 Introduction to languages... 3 Alphabets... 3 Strings...

Table of contents: Lecture N Summary... 3 What does automata mean?... 3 Introduction to languages... 3 Alphabets... 3 Strings... Tle of contents: Lecture N0.... 3 ummry... 3 Wht does utomt men?... 3 Introduction to lnguges... 3 Alphets... 3 trings... 3 Defining Lnguges... 4 Lecture N0. 2... 7 ummry... 7 Kleene tr Closure... 7 Recursive

More information

CMSC 330: Organization of Programming Languages. DFAs, and NFAs, and Regexps (Oh my!)

CMSC 330: Organization of Programming Languages. DFAs, and NFAs, and Regexps (Oh my!) CMSC 330: Orgniztion of Progrmming Lnguges DFAs, nd NFAs, nd Regexps (Oh my!) CMSC330 Spring 2018 Types of Finite Automt Deterministic Finite Automt (DFA) Exctly one sequence of steps for ech string All

More information

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018 Finite Automt Theory nd Forml Lnguges TMV027/DIT321 LP4 2018 Lecture 10 An Bove April 23rd 2018 Recp: Regulr Lnguges We cn convert between FA nd RE; Hence both FA nd RE ccept/generte regulr lnguges; More

More information

Date Lesson Text TOPIC Homework. Solving for Obtuse Angles QUIZ ( ) More Trig Word Problems QUIZ ( )

Date Lesson Text TOPIC Homework. Solving for Obtuse Angles QUIZ ( ) More Trig Word Problems QUIZ ( ) UNIT 5 TRIGONOMETRI RTIOS Dte Lesson Text TOPI Homework pr. 4 5.1 (48) Trigonometry Review WS 5.1 # 3 5, 9 11, (1, 13)doso pr. 6 5. (49) Relted ngles omplete lesson shell & WS 5. pr. 30 5.3 (50) 5.3 5.4

More information

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS CS 310 (sec 20) - Winter 2003 - Finl Exm (solutions) SOLUTIONS 1. (Logic) Use truth tles to prove the following logicl equivlences: () p q (p p) (q q) () p q (p q) (p q) () p q p q p p q q (q q) (p p)

More information

Lesson 1: Quadratic Equations

Lesson 1: Quadratic Equations Lesson 1: Qudrtic Equtions Qudrtic Eqution: The qudrtic eqution in form is. In this section, we will review 4 methods of qudrtic equtions, nd when it is most to use ech method. 1. 3.. 4. Method 1: Fctoring

More information

CHAPTER 1 Regular Languages. Contents. definitions, examples, designing, regular operations. Non-deterministic Finite Automata (NFA)

CHAPTER 1 Regular Languages. Contents. definitions, examples, designing, regular operations. Non-deterministic Finite Automata (NFA) Finite Automt (FA or DFA) CHAPTER Regulr Lnguges Contents definitions, exmples, designing, regulr opertions Non-deterministic Finite Automt (NFA) definitions, equivlence of NFAs DFAs, closure under regulr

More information

A-Level Mathematics Transition Task (compulsory for all maths students and all further maths student)

A-Level Mathematics Transition Task (compulsory for all maths students and all further maths student) A-Level Mthemtics Trnsition Tsk (compulsory for ll mths students nd ll further mths student) Due: st Lesson of the yer. Length: - hours work (depending on prior knowledge) This trnsition tsk provides revision

More information

Where did dynamic programming come from?

Where did dynamic programming come from? Where did dynmic progrmming come from? String lgorithms Dvid Kuchk cs302 Spring 2012 Richrd ellmn On the irth of Dynmic Progrmming Sturt Dreyfus http://www.eng.tu.c.il/~mi/cd/ or50/1526-5463-2002-50-01-0048.pdf

More information

CS S-12 Turing Machine Modifications 1. When we added a stack to NFA to get a PDA, we increased computational power

CS S-12 Turing Machine Modifications 1. When we added a stack to NFA to get a PDA, we increased computational power CS411-2015S-12 Turing Mchine Modifictions 1 12-0: Extending Turing Mchines When we dded stck to NFA to get PDA, we incresed computtionl power Cn we do the sme thing for Turing Mchines? Tht is, cn we dd

More information

Goals: Determine how to calculate the area described by a function. Define the definite integral. Explore the relationship between the definite

Goals: Determine how to calculate the area described by a function. Define the definite integral. Explore the relationship between the definite Unit #8 : The Integrl Gols: Determine how to clculte the re described by function. Define the definite integrl. Eplore the reltionship between the definite integrl nd re. Eplore wys to estimte the definite

More information

Designing Information Devices and Systems I Spring 2018 Homework 7

Designing Information Devices and Systems I Spring 2018 Homework 7 EECS 16A Designing Informtion Devices nd Systems I Spring 2018 omework 7 This homework is due Mrch 12, 2018, t 23:59. Self-grdes re due Mrch 15, 2018, t 23:59. Sumission Formt Your homework sumission should

More information

Linear Systems with Constant Coefficients

Linear Systems with Constant Coefficients Liner Systems with Constnt Coefficients 4-3-05 Here is system of n differentil equtions in n unknowns: x x + + n x n, x x + + n x n, x n n x + + nn x n This is constnt coefficient liner homogeneous system

More information

5.2 Volumes: Disks and Washers

5.2 Volumes: Disks and Washers 4 pplictions of definite integrls 5. Volumes: Disks nd Wshers In the previous section, we computed volumes of solids for which we could determine the re of cross-section or slice. In this section, we restrict

More information

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4 Intermedite Mth Circles Wednesdy, Novemer 14, 2018 Finite Automt II Nickols Rollick nrollick@uwterloo.c Regulr Lnguges Lst time, we were introduced to the ide of DFA (deterministic finite utomton), one

More information

Module 9: Tries and String Matching

Module 9: Tries and String Matching Module 9: Tries nd String Mtching CS 240 - Dt Structures nd Dt Mngement Sjed Hque Veronik Irvine Tylor Smith Bsed on lecture notes by mny previous cs240 instructors Dvid R. Cheriton School of Computer

More information

Module 9: Tries and String Matching

Module 9: Tries and String Matching Module 9: Tries nd String Mtching CS 240 - Dt Structures nd Dt Mngement Sjed Hque Veronik Irvine Tylor Smith Bsed on lecture notes by mny previous cs240 instructors Dvid R. Cheriton School of Computer

More information

Preview 11/1/2017. Greedy Algorithms. Coin Change. Coin Change. Coin Change. Coin Change. Greedy algorithms. Greedy Algorithms

Preview 11/1/2017. Greedy Algorithms. Coin Change. Coin Change. Coin Change. Coin Change. Greedy algorithms. Greedy Algorithms Preview Greed Algorithms Greed Algorithms Coin Chnge Huffmn Code Greed lgorithms end to e simple nd strightforwrd. Are often used to solve optimiztion prolems. Alws mke the choice tht looks est t the moment,

More information

Formal Methods in Software Engineering

Formal Methods in Software Engineering Forml Methods in Softwre Engineering Lecture 09 orgniztionl issues Prof. Dr. Joel Greenyer Decemer 9, 2014 Written Exm The written exm will tke plce on Mrch 4 th, 2015 The exm will tke 60 minutes nd strt

More information

Special Numbers, Factors and Multiples

Special Numbers, Factors and Multiples Specil s, nd Student Book - Series H- + 3 + 5 = 9 = 3 Mthletics Instnt Workooks Copyright Student Book - Series H Contents Topics Topic - Odd, even, prime nd composite numers Topic - Divisiility tests

More information

Lecture 2: January 27

Lecture 2: January 27 CS 684: Algorithmic Gme Theory Spring 217 Lecturer: Év Trdos Lecture 2: Jnury 27 Scrie: Alert Julius Liu 2.1 Logistics Scrie notes must e sumitted within 24 hours of the corresponding lecture for full

More information

Section 6: Area, Volume, and Average Value

Section 6: Area, Volume, and Average Value Chpter The Integrl Applied Clculus Section 6: Are, Volume, nd Averge Vlue Are We hve lredy used integrls to find the re etween the grph of function nd the horizontl xis. Integrls cn lso e used to find

More information

Parse trees, ambiguity, and Chomsky normal form

Parse trees, ambiguity, and Chomsky normal form Prse trees, miguity, nd Chomsky norml form In this lecture we will discuss few importnt notions connected with contextfree grmmrs, including prse trees, miguity, nd specil form for context-free grmmrs

More information

List all of the possible rational roots of each equation. Then find all solutions (both real and imaginary) of the equation. 1.

List all of the possible rational roots of each equation. Then find all solutions (both real and imaginary) of the equation. 1. Mth Anlysis CP WS 4.X- Section 4.-4.4 Review Complete ech question without the use of grphing clcultor.. Compre the mening of the words: roots, zeros nd fctors.. Determine whether - is root of 0. Show

More information

Exercise 5.5: Large-scale log-normal fading

Exercise 5.5: Large-scale log-normal fading Exercise 5.5: Lrge-scle log-norml fding Since the system is designed to hndle propgtion loss of 135 db, outge will hppen when the propgtion loss is 8 db higher thn the deterministic loss of 17 db 135 17

More information

10.2 The Ellipse and the Hyperbola

10.2 The Ellipse and the Hyperbola CHAPTER 0 Conic Sections Solve. 97. Two surveors need to find the distnce cross lke. The plce reference pole t point A in the digrm. Point B is meters est nd meter north of the reference point A. Point

More information

Using QM for Windows. Using QM for Windows. Using QM for Windows LEARNING OBJECTIVES. Solving Flair Furniture s LP Problem

Using QM for Windows. Using QM for Windows. Using QM for Windows LEARNING OBJECTIVES. Solving Flair Furniture s LP Problem LEARNING OBJECTIVES Vlu%on nd pricing (November 5, 2013) Lecture 11 Liner Progrmming (prt 2) 10/8/16, 2:46 AM Olivier J. de Jong, LL.M., MM., MBA, CFD, CFFA, AA www.olivierdejong.com Solving Flir Furniture

More information

Computing with finite semigroups: part I

Computing with finite semigroups: part I Computing with finite semigroups: prt I J. D. Mitchell School of Mthemtics nd Sttistics, University of St Andrews Novemer 20th, 2015 J. D. Mitchell (St Andrews) Novemer 20th, 2015 1 / 34 Wht is this tlk

More information

12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2016

12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2016 CS125 Lecture 12 Fll 2016 12.1 Nondeterminism The ide of nondeterministic computtions is to llow our lgorithms to mke guesses, nd only require tht they ccept when the guesses re correct. For exmple, simple

More information

Regular expressions, Finite Automata, transition graphs are all the same!!

Regular expressions, Finite Automata, transition graphs are all the same!! CSI 3104 /Winter 2011: Introduction to Forml Lnguges Chpter 7: Kleene s Theorem Chpter 7: Kleene s Theorem Regulr expressions, Finite Automt, trnsition grphs re ll the sme!! Dr. Neji Zgui CSI3104-W11 1

More information

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir)

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir) BİL 354 Veritnı Sistemleri Reltionl lger (İlişkisel Ceir) Reltionl Queries Query lnguges: llow mnipultion nd retrievl of dt from dtse. Reltionl model supports simple, powerful QLs: Strong forml foundtion

More information

Harvard University Computer Science 121 Midterm October 23, 2012

Harvard University Computer Science 121 Midterm October 23, 2012 Hrvrd University Computer Science 121 Midterm Octoer 23, 2012 This is closed-ook exmintion. You my use ny result from lecture, Sipser, prolem sets, or section, s long s you quote it clerly. The lphet is

More information

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton 25. Finite Automt AUTOMATA AND LANGUAGES A system of computtion tht only hs finite numer of possile sttes cn e modeled using finite utomton A finite utomton is often illustrted s stte digrm d d d. d q

More information

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations. Lecture 3 3 Solving liner equtions In this lecture we will discuss lgorithms for solving systems of liner equtions Multiplictive identity Let us restrict ourselves to considering squre mtrices since one

More information

Consolidation Worksheet

Consolidation Worksheet Cmbridge Essentils Mthemtics Core 8 NConsolidtion Worksheet N Consolidtion Worksheet Work these out. 8 b 7 + 0 c 6 + 7 5 Use the number line to help. 2 Remember + 2 2 +2 2 2 + 2 Adding negtive number is

More information

19 Optimal behavior: Game theory

19 Optimal behavior: Game theory Intro. to Artificil Intelligence: Dle Schuurmns, Relu Ptrscu 1 19 Optiml behvior: Gme theory Adversril stte dynmics hve to ccount for worst cse Compute policy π : S A tht mximizes minimum rewrd Let S (,

More information

Section: Other Models of Turing Machines. Definition: Two automata are equivalent if they accept the same language.

Section: Other Models of Turing Machines. Definition: Two automata are equivalent if they accept the same language. Section: Other Models of Turing Mchines Definition: Two utomt re equivlent if they ccept the sme lnguge. Turing Mchines with Sty Option Modify δ, Theorem Clss of stndrd TM s is equivlent to clss of TM

More information

CSC 473 Automata, Grammars & Languages 11/9/10

CSC 473 Automata, Grammars & Languages 11/9/10 CSC 473 utomt, Grmmrs & Lnguges 11/9/10 utomt, Grmmrs nd Lnguges Discourse 06 Decidbility nd Undecidbility Decidble Problems for Regulr Lnguges Theorem 4.1: (embership/cceptnce Prob. for DFs) = {, w is

More information