lecture 10
play

Lecture 10: Sequential Networks: Implementation (Review) CSE 140: - PowerPoint PPT Presentation

Oct 03, 2023 •386 likes •717 views

Lecture 10: Sequential Networks: Implementation (Review) CSE 140: Components and Design Techniques for Digital Systems Spring 2014 CK Cheng, Diba Mirza Dept. of Computer Science and Engineering University of California, San Diego 1

  1. Lecture 10: Sequential Networks: Implementation (Review) CSE 140: Components and Design Techniques for Digital Systems Spring 2014 CK Cheng, Diba Mirza Dept. of Computer Science and Engineering University of California, San Diego 1

  2. Universal*Gates*(review)* • Is*the*following*func7on*a*universal*set* ****f(x,y)*=*xy*+x’y’* ****A.*Yes* ****B.*No* * * 2

  3. Universal*Gates*(review)* • Is*the*following*func7on*a*universal*set* ****f(x,y,z)*=*xyz*+x’y’z’* ****A.*Yes* ****B.*No* * * 3

  4. Moore Design Output is a function of present state only

  5. Mealy Design Output is a function of present state and input

  6. Implementation Procedure 1. Draw*State*Diagram* [Mealy*or*Moore*as*required] ** * 2.*Assign*the*state,*input,*output*encoding* * 3.*Draw*State*Transi7on*Table** * 4.*Derive*Boolean*Expressions* * 5.*Draw*circuit** *

  7. Pattern Recognizer: A sequential machine has a binary input x in {a,b}. For x(t-2, t) = baa, the output y(t) = 1, otherwise y(t) = 0. Implement the corresponding circuit using JK flip flops Draw the Moore and Mealy state diagram 7

  8. STEP 1 : Moore State Diagram a a b b a a S0 S1 S2 S3 0 1 0 0 b b x(t) y(t) C1 C2 CLK S(t) Moore Machine

  9. STEP 1 : Mealy State Diagram a/1 a/0 Looking for baa b/0 b/0 a/0 S0 S1 S2 b/0 x(t) y(t) C1 C2 CLK S(t) Mealy Machine

  10. STEP 2 : State, Input and Output Encoding State Encoding a/1 a/0 b/0 S0: S1: b/0 a/0 S0 S1 S2 S2: b/0 Input Encoding a: 0 b: 1 PI Q: How many binary variables are needed to encode the states in the above diagram A. Three B. Two C. One

  11. STEP 3: State Table State Encoding 0/1 0/0 Q 1 Q 0 1/0 S0: 0 0 1/0 0/0 S1: 0 1 00 01 10 1/0 S2: 1 0 X(t) Q 1 (t) Q 0 (t) 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 1 1 0

  12. STEP 3: State Table State Assignment 0/1 0/0 Q 1 Q 0 1/0 S0: 0 0 1/0 0/0 S1: 0 1 00 01 10 1/0 S2: 1 0 id x(t) Q 1 (t) Q 0 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 0 0 1 1 0 0 0 1 0 2 0 0 1 1 0 0 3 1 0 1 0 1 0 4 0 1 0 0 0 1 5 1 1 0 0 1 0

  13. Mapping the variables to the Mealy circuit id x(t) Q 1 (t) Q 0 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 0 0 …… …… ……………...... ………………… .………….... …… …... ....................... ………………… ……………. C2 C1

  14. Excitation table using JK flip flop id x(t) Q 1 (t) Q 0 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 0 0 … …... ………… …………………… ………….... … ....... …............. ……………… …………… x(t) Q 0 (t) y(t) JK C2 C1 Q 1 (t) JK

  15. Excitation table using JK flip flop id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 0 0 1 1 0 0 0 1 0 2 0 0 1 1 0 0 3 1 0 1 0 1 0 4 0 1 0 0 0 1 5 1 1 0 0 1 0 PI Q: Which of the following completely specifies the value of J 0 (t) in the above table? JK Q(t+1) NS A. Q 0 (t) PS 0 1 B. Q 1 (t), Q 0 (t) 0- 1- 0 1 Q(t) -1 -0 C. Q 0 (t), Q 0 (t+1) D. None of the above

  16. Excitation table using JK flip flop id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 X 0 X 0 0 0 1 1 0 0 1 X 0 X 0 1 0 2 0 0 1 X 1 1 X 1 0 0 3 1 0 1 X 0 0 X 0 1 0 4 0 1 0 0 X X 1 0 0 1 5 1 1 0 1 X X 1 0 1 0 JK Q(t+1) NS PS 0 1 0- 1- 0 1 Q(t) -1 -0

  17. STEP 4, 5: Boolean Expressions, Circuit id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 1 0 0 1 0 0 0 … …... ………… …………………… ………….... … ....... …............. ……………… …………… x(t) Q 0 (t) y(t) JK C2 C1 Q 1 (t) JK

  18. STEP 4: Boolean Expressions id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 X 0 X 0 0 0 1 1 0 0 1 X 0 X 0 1 0 2 0 0 1 X 1 1 X 1 0 0 3 1 0 1 X 0 0 X 0 1 0 4 0 1 0 0 X X 1 0 0 1 5 1 1 0 1 X X 1 0 1 0 K-map for J 0 (t): 0 2 6 4 1 3 7 5

  19. STEP 4: Boolean Expressions id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 X 0 X 0 0 0 1 1 0 0 1 X 0 X 0 1 0 2 0 0 1 X 1 1 X 1 0 0 3 1 0 1 X 0 0 X 0 1 0 4 0 1 0 0 X X 1 0 0 1 5 1 1 0 1 X X 1 0 1 0 Q 1 (t) Q 0 (t) K-map for J 0 (t): x(t) 0 2 6 4 0 X X 0 J 0 (t)= x(t) 1 3 7 5 1 X X 1

  20. STEP 4: Boolean Expressions id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 X 0 X 0 0 0 1 1 0 0 1 X 0 X 0 1 0 2 0 0 1 X 1 1 X 1 0 0 3 1 0 1 X 0 0 X 0 1 0 4 0 1 0 0 X X 1 0 0 1 5 1 1 0 1 X X 1 0 1 0 J 0 (t)= x(t) K 0 (t)= x(t)’ J 1 (t)= x(t)’Q 0 (t) K 1 (t)= 1

  21. STEP 5: Next state Logic id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 0 0 … …... ………… …………………… ………….... … ....... …............. ……………… …………… x(t) Q 0 (t) y(t) JK C2 J 0 (t)= x(t) K 0 (t)= x(t)’ Q 1 (t) J 1 (t)= x(t)’Q 0 (t) JK K 1 (t)= 1

  22. STEP 5: Output logic id x(t) Q 1 (t) Q 0 (t) J 0 (t) K 0 (t) J 1 (t) K 1 (t) Q 1 (t+1) Q 0 (t+1) y(t) 0 0 0 0 0 0 0 … …... ………… …………………… ………….... … ....... …............. ……………… …………… x(t) Q 0 (t) J 0 (t)= x(t) y(t) K 0 (t)= x(t)’ JK J 1 (t)= x(t)’Q 0 (t) K 1 (t)= 1 Q 1 (t) JK y(t)= x(t)’Q 1 (t)

  23. Modified 2 bit counter x(t) Q D Q’ Q 0 (t) Q Q 0 (t) D Q’ Q 1 (t) y(t) Q 1 (t) CLK Q: Is the above circuit a Moore or a Mealy? A. Moore B. Mealy 23

  24. Free running 2-bit counter using T-flip flops S 0 * S 3 * S 1 * S 2 * Excitation table id Q 1 (t) Q 0 (t) T 1 (t) T 0 (t) Q 1 (t+1) Q 0 (t+1) 0 0 0 0 1 0 1 1 0 1 1 1 1 0 2 1 0 0 1 1 1 3 1 1 1 1 0 0 24

  25. Free running counter with T flip flops Q 0 T 0 (t) = 1 1 Q T T 1 (t) = Q 0 (t) Q’ Q Q 1 T Q’ T 1 Q: Notice Q 1 is not fed back into the circuit. Does this mean we have only one state variable? A. Yes B. No 25

  26. Problem 2-II of exercise 4 Q: A state machine is described by the following equations: Q 1 ( t + 1) = Q 0 ( t ) + Q ′ 1 ( t ) x ( t ), Q 0 ( t + 1) = Q 1 ( t ) � x ( t ) + Q 0 ( t ) x ( t ), y ( t ) = Q ′ 1 ( t ) Q 0 ( t ) + x ( t ) a) Write the state table b) Design the system with two T-flip flops and a minimal AND-OR-NOT network c) Design the system with two SR-flip flops and a minimal AND-OR-NOT network 26

  27. Q 1 ( t + 1) = Q 0 ( t ) + Q ′ 1 ( t ) x ( t ), Q 0 ( t + 1) = Q 1 ( t ) � x ( t ) + Q 0 ( t ) x ( t ), y ( t ) = Q ′ 1 ( t ) Q 0 ( t ) + x ( t ) id x(t) Q 1 (t) Q 0 (t) Q 1 (t+1) Q 0 (t+1) 0 0 0 0 1 0 0 1 2 0 1 0 3 0 1 1 4 1 0 0 5 1 0 1 6 1 1 0 7 1 1 1 27

  28. What is the next step to arrive at the circuit implementation? A. Draw the state diagram of the problem B. Obtain the excitation table C. Get the reduced expression for the next state as a function of present state and inputs id x(t) Q 1 (t) Q 0 (t) Q 1 (t+1) Q 0 (t+1) 0**************0** 0 0 0 0 1**************0* 1 0 0 1 0**************1* 2 0 1 0 1**************1* 3 0 1 1 1**************1* 4 1 0 0 1**************1* 5 1 0 1 6 1 1 0 0 0 7 1 1 1 1 1 28

  29. SR Q(t+1) NS PS 0 1 0 0- 10 1 01 -0 Q(t) id x(t) Q 1 (t) Q 0 (t) S 0 (t) R 0 (t) S 1 (t)R 1 (t) Q 1 (t+1) Q 0 (t+1) 0**************0** 0 0 0 0 1**************0* 1 0 0 1 0**************1* 2 0 1 0 1**************1* 3 0 1 1 1**************1* 4 1 0 0 1**************1* 5 1 0 1 6 1 1 0 0 0 7 1 1 1 1 1 29

Recommend

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then

Lecture # 5 - Monday, Aug 30th In this lecture I reviewed the previous lecture 4, and then reviewed the final point of lecture #3: Hennigs logical approach to tree inference is valid as logic, but we have a hard time using it because our data

660 views • 7 slides
Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13

Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13

TU/e Algorithms (2IL15) Lecture 13 Algorithms (2IL15) Lecture 13 Wrap-up lecture 1 TU/e Algorithms (2IL15) Lecture 13 Part I of the course: Optimization Problems we want to find valid solution minimizing cost (or maximizing

904 views • 20 slides
In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken

In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken

In 2020SP, this lecture and lecture 20 are both optional extra material CS 5412/LECTURE 17 Ken Birman LEAVE NO TRACE BEHIND Spring, 2020 HTTP://WWW.CS.CORNELL.EDU/COURSES/CS5412/2020SP 1 THE PRIVACY PUZZLE FOR I O T We have sensors

1.04k views • 65 slides
Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface

Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface

Recall last lecture ... Lecture 8 Also last lecture: Painter's Algorithm More Hidden Surface Removal Sort polygons in depth. Use the farthest vertex in each polygon as the sorting key. Efficient Painter - binary space partition (BSP)

563 views • 6 slides
Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 -

Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 -

Plan Lecture 1 - String diagrams and symmetric monoidal categories Lecture 2 - Resource-sensitive algebraic theories Lecture 3 - Interacting Hopf monoids and graphical linear algebra Lecture 4 - Signal Flow Graphs and recurrence

1.2k views • 61 slides
Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A

Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A

Where are we at - Topic overview Lecture 1A: Security requirements/features Lecture 7A Threatens Privacy Threatens Try to achieve Lecture 2B: Network threats Lecture 3A: Attacks on Lecture 6A: Security Protocols Web servers, malware

381 views • 25 slides
Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture

Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture

ISDS & Digital Business Skills Lecture Capture Introduction to Lecture Capture Learning Outcomes What will lecture capture actually do? What happens before a lecture? What happens during a lecture? What does the button do?

527 views • 18 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

1.74k views • 60 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

850 views • 46 slides
CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How

CSE 158 Lecture 4 Web Mining and Recommender Systems More Classifiers Last lecture How can we predict binary or categorical variables? {0,1}, {True, False} {1, , N} Last lecture Will I purchase this product? (yes) Will I click

839 views • 58 slides
Usability of Programming Languages Lecture 4 - directed by your research interests Lecture

Usability of Programming Languages Lecture 4 - directed by your research interests Lecture

Overview Practical experimental course lectures are only introductory Lecture 1 - theoretical principles classic approaches l h current trends in leading research. Lecture 2 - candidate research methods advantages and

499 views • 15 slides
Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture

Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture

Introduction to AI & Intelligent Agents This Lecture Chapters 1 and 2 Next Lecture Chapter 3.1 to 3.4 (Please read lecture topic material before and after each lecture on that topic) What is Artificial Intelligence? Thought

636 views • 31 slides
Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is

Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is

Introduction to Numerical Optimization Biostatistics 615/815 Lecture 14 Lecture 14 Course is More Than Half Done! If you have comments they are very welcome y y Lectures Lecture notes Weekly Homework Midterm

795 views • 42 slides
Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma

Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma

Lecture 12: Clustering 1 6.0002 LECTURE 12 Re Reading Chapter 23 6.0002 LECTURE 12 2 Mach Ma chine e Lea earn rning Paradigm Observe set of examples: training data Infer something about process that generated that data Use

847 views • 36 slides
Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4

Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4

Lecture Outline Regeltechniek Previous lecture: Stability and transient response. Lecture 4 Basics of Feedback Control Robert Babu ska Today: Steady-state response. Delft Center for Systems and Control Faculty of Mechanical

388 views • 7 slides
Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students

Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students

Psycholinguistics Lecture 2 By Dr.Chelli Lecture Objectives At the end of this lecture, students will be able to: Discuss the different stages in the history of psycholinguistics Identify the scholars in the field of psycholinguistics

589 views • 28 slides
Methodology for Lecture Methodology for Lecture Computer Graphics (Spring 2008) Computer

Methodology for Lecture Methodology for Lecture Computer Graphics (Spring 2008) Computer

Methodology for Lecture Methodology for Lecture Computer Graphics (Spring 2008) Computer Graphics (Spring 2008) Lecture deals with lighting (teapot shaded as in HW1) Some Nate Robbins tutor demos in lecture COMS 4160, Lecture 14: OpenGL

535 views • 5 slides
Lecture Outline Regeltechniek Previous lecture: Nyquist plot and stability criterion. Lecture 11

Lecture Outline Regeltechniek Previous lecture: Nyquist plot and stability criterion. Lecture 11

Lecture Outline Regeltechniek Previous lecture: Nyquist plot and stability criterion. Lecture 11 State-space models and state feedback control (We are finished with frequency domain methods.) Robert Babu ska Today: Delft Center for

452 views • 6 slides
CSE Fall 2014 311 Lecture 1 Lecture 1 Lecture 1: Propositional Logic Lecture 1 Foundations

CSE Fall 2014 311 Lecture 1 Lecture 1 Lecture 1: Propositional Logic Lecture 1 Foundations

CSE 311: Foundations of Computing I CSE Fall 2014 311 Lecture 1 Lecture 1 Lecture 1: Propositional Logic Lecture 1 Foundations of Computing I Fall 2014 Some Perspective About the Course We will study the theory needed for CSE: Computer

242 views • 7 slides
Proteomics Steven Meinhardt Lectures Lecture 1 Introduction review of proteins

Proteomics Steven Meinhardt Lectures Lecture 1 Introduction review of proteins

11/29/2012 Proteomics Steven Meinhardt Lectures Lecture 1 Introduction review of proteins Lecture 2 Gel Electrophoresis Lecture 3 Mass Spectrometry Lecture 4 Programs for MS analysis Lecture 5 MIAPE (Minimum

900 views • 8 slides
Multiphase Modelling in Cancer Helen Byrne Wolfson Centre for Mathematical Biology Mathematical

Multiphase Modelling in Cancer Helen Byrne Wolfson Centre for Mathematical Biology Mathematical

Lecture 1 Lecture 2 Lecture 3 Lecture 4 Multiphase Modelling in Cancer Helen Byrne Wolfson Centre for Mathematical Biology Mathematical Institute University of Oxford CRM, Barcelona, April 2018 Lecture 1 Lecture 2 Lecture 3 Lecture 4

1.79k views • 141 slides
Lecture 1: Neurons Lecture 2: Coding with spikes Lecture 3: Tuning curves and receptive fields

Lecture 1: Neurons Lecture 2: Coding with spikes Lecture 3: Tuning curves and receptive fields

Lecture 1: Neurons Lecture 2: Coding with spikes Lecture 3: Tuning curves and receptive fields Lecture 4: Population vectors To gain a basic understanding of how population vectors are constructed 4 credit students: First assignment is

584 views • 31 slides
Algorithms (2IL15) Lecture 10 NP-Completeness, II 1 TU/e Algorithms (2IL15) Lecture 10

Algorithms (2IL15) Lecture 10 NP-Completeness, II 1 TU/e Algorithms (2IL15) Lecture 10

TU/e Algorithms (2IL15) Lecture 10 Algorithms (2IL15) Lecture 10 NP-Completeness, II 1 TU/e Algorithms (2IL15) Lecture 10 Summary of previous lecture P: class of decision problems that can be solved in polynomial time NP:

496 views • 34 slides
Lecture 1: Bioinformatic Algorithms In this lecture Logistics of the course

Lecture 1: Bioinformatic Algorithms In this lecture Logistics of the course

Lecture 1: Bioinformatic Algorithms In this lecture Logistics of the course Introduction to basic biology which will continue in the following lecture Logistics of the Course Logistics About the Course Christina Boucher: (email)

546 views • 40 slides

More recommend