Open AccessBook
Sequential Logic Synthesis
Pranav Ashar,Srinivas Devadas,A. Richard Newton +2 more
- 30 Nov 1991
122
TL;DR: This chapter discusses the design and implementation of VLSI for Sequential Logic Synthesis, and the development of heuristics to Minimize the Number of Encoding Bits.
read more
Abstract: Acknowledgments.- 1 Introduction.- 1.1 Computer-Aided VLSI Design.- 1.2 The Synthesis Pipeline.- 1.3 Sequential Logic Synthesis.- 1.4 Early Work in Sequential Logic Synthesis.- 1.5 Recent Developments.- 1.5.1 State Encoding.- 1.5.2 Finite State Machine Decomposition.- 1.5.3 Sequential Don't Cares.- 1.5.4 Sequential Resynthesis at the Logic Level.- 1.6 Organization of the Book.- 2 Basic Definitions and Concepts.- 2.1 Two-Valued Logic.- 2.2 Multiple-Valued Logic.- 2.3 Multilevel Logic.- 2.4 Multiple-Valued Input, Multilevel Logic.- 2.5 Finite Automata.- 3 Encoding of Symbolic Inputs.- 3.1 Introduction.- 3.2 Input Encoding Targeting Two-Level Logic.- 3.2.1 One-Hot Coding and Multiple-Valued Minimization.- 3.2.2 Input Constraints and Face Embedding.- 3.3 Satisfying Encoding Constraints.- 3.3.1 Definitions.- 3.3.2 Column-Based Constraint Satisfaction.- 3.3.3 Row-Based Constraint Satisfaction.- 3.3.4 Constraint Satisfaction Using Dichotomies.- 3.3.5 Simulated Annealing for Constraint Satisfaction.- 3.4 Input Encoding Targeting Multilevel Logic.- 3.4.1 Kernels and Kernel Intersections.- 3.4.2 Kernels and Multiple-Valued Variables.- 3.4.3 Multiple-Valued Factorization.- 3.4.4 Size Estimation in Algebraic Decomposition.- 3.4.5 The Encoding Step.- 3.5 Conclusion.- 4 Encoding of Symbolic Outputs.- 4.1 Heuristic Output Encoding Targeting Two-Level Logic.- 4.1.1 Dominance Relations.- 4.1.2 Output Encoding by the Derivation of Dominance Relations.- 4.1.3 Heuristics to Minimize the Number of Encoding Bits.- 4.1.4 Disjunctive Relationships.- 4.1.5 Summary.- 4.2 Exact Output Encoding Targeting Two-Level Logic.- 4.2.1 Generation of Generalized Prime Implicants.- 4.2.2 Selecting a Minimum Encodeable Cover.- 4.2.3 Dominance and Disjunctive Relationships to Sat-isfy Constraints.- 4.2.4 Constructing the Optimized Cover.- 4.2.5 Correctness of the Procedure.- 4.2.6 Multiple Symbolic Outputs.- 4.2.7 The Issue of the All Zeros Code.- 4.2.8 Reduced Prime Immplicant Table Generation.- 4.2.9 Covering with Encodeability Constraints.- 4.2.10 Experimental Results Using the Exact Algorithm.- 4.2.11 Computationally Efficient Heuristic Minimization.- 4.3 Symbolic Output Don't Cares.- 4.4 Output Encoding for Multilevel Logic.- 4.5Conclusion.- 5 State Encoding.- 5.1 Heuristic State Encoding Targeting Two-Level Logic.- 5.1.1 Approximating State Encoding as Input Encoding.- 5.1.2 Constructing Input and Dominance Relations.- 5.1.3 Heuristics to Minimize the Number of Encoding Bits.- 5.1.4 Alternate Heuristic State Encoding Strategies.- 5.2 Exact State Encoding for Two-Level Logic.- 5.2.1 Generation of Generalized Prime Implicants.- 5.2.2 Selecting a Minimum Encodeable Cover.- 5.2.3 Constructing an Optimized Cover.- 5.2.4 Reduced Prime Implicant Table Generation.- 5.2.5 The Covering Step.- 5.2.6 Heuristics to Minimize the Number of Encoding Bits.- 5.2.7 Encoding Via Boolean Satisfiability.- 5.3 Symbolic Next State Don't Cares.- 5.4 State Encoding for Multilevel Logic.- 5.4.1 Introduction.- 5.4.2 Modeling Common Cube Extraction.- 5.4.3 A Fanout-Oriented Algorithm.- 5.4.4 A Fanin-Oriented Algorithm.- 5.4.5 The Embedding Algorithm.- 5.4.6 Improvements to Estimation Strategies.- 5.5 Conclusion.- 6 Finite State Machine Decomposition.- 6.1 Introduction.- 6.2 Definitions for Decomposition.- 6.3 Preserved Covers and Partitions.- 6.4 General Decomposition Using Factors.- 6.4.1 Introduction.- 6.4.2 An Example Factorization.- 6.4.3 Defining An Exact Factor.- 6.4.4 Exact Factorization.- 6.4.5 Identifying Good Factors.- 6.4.6 Limitations of the Factoring Approach.- 6.5 Exact Decomposition Procedure for a Two-Way General Topology.- 6.5.1 The Cost Function.- 6.5.2 Formulation of Optimum Decomposition.- 6.5.3 Relationship to Partition Algebra.- 6.5.4 Relationship to Factorization.- 6.5.5 Generalized Prime Implicant Generation.- 6.5.6 Encodeability of a GPI Cover.- 6.5.7 Correctness of the Exact Algorithm.- 6.5.8 Checking for Output Constraint Violations.- 6.5.9 Checking for Input Constraint Violations.- 6.5.10 Relative Complexity of Encodeability Checking.- 6.5.11 The Covering Step in Exact Decomposition.- 6.6 Targeting Arbitrary Topologies.- 6.6.1 Cascade Decompositions.- 6.6.2 Parallel Decompositions.- 6.6.3 Arbitrary Decompositions.- 6.6.4 Exactness of the Decomposition Procedure.- 6.7 Heuristic General Decomposition.- 6.7.1 Overview.- 6.7.2 Minimization of Covers and Removal of Constraint Violations.- 6.7.3 Symbolic-expand.- 6.7.4 Symbolic-reduce.- 6.8 Relationship to State Assignment.- 6.9 Experimental Results.- 6.10 Conclusion.- 7 Sequential Don't Cares.- 7.1 Introduction.- 7.2 State Minimization.- 7.2.1 Generating the Implication Table.- 7.2.2 Completely-Specified Machines.- 7.2.3 Incompletely-Specified Machines.- 7.2.4 Dealing with Exponentially-Sized Input Alphabets.- 7.3 Input Don't Care Sequences.- 7.3.1 Input Don't Care Vectors.- 7.3.2 Input Don't Care Sequences to Minimize States.- 7.3.3 Exploiting Input Don't Care Sequences.- 7.3.4 Early Work on Input Don't Care Sequences.- 7.4 Output Don't Cares to Minimize States.- 7.4.1 Exploiting Output Don't Cares.- 7.5 Single Machine Optimization at the Logic Level.- 7.5.1 Introduction.- 7.5.2 Invalid State and Unspecified Edge Don't Cares.- 7.5.3 Equivalent State Don't Cares.- 7.5.4 Boolean Relations Due To Equivalent States.- 7.5.5 Minimization With Don't Cares and Boolean Re-lations.- 7.6 Interconnected Machine Optimization at the Logic Level.- 7.6.1 Unconditional Compatibility.- 7.6.2 Conditional Compatibility.- 7.6.3 Invalid States and Edges.- 7.6.4 Searching for Unreachability and Compatibility.- 7.7 Conclusion.- 8 Conclusions and Directions for Future Work.- 8.1 Alternate Representations.- 8.2 Optimization at the Logic Level.- 8.3 Don't Cares and Testability.- 8.4 Exploiting Register-Transfer Level Information.- 8.5 Sequential Logic Synthesis Systems.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Survey of Optimization Techniques Targeting Low Power VLSI Circuits
Srinivas Devadas,Sharad Malik +1 more
- 01 Jan 1995
TL;DR: This work surveys state-of-the-art optimization methods that target low power dissipation in VLSI circuits and considers the circuit, logic, architectural and system levels.
State assignment for low power dissipation
Luca Benini,G. De Micheli +1 more
TL;DR: A general theoretic framework for the solution of the state assignment problem is formulated, and different algorithms trading off computational effort for quality are proposed, resulting in a 16% average reduction in switching activity.
186
Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems
C.L. Giles,Christian W. Omlin,K.K. Thornber +2 more
- 01 Sep 1999
TL;DR: In this article, a synthesis method is proposed for mapping fuzzy finite state automata (FFA) into recurrent neural networks, which is suitable for direct implementation in very large scale integration (VLSI) systems.
Learning a class of large finite state machines with a recurrent neural network
TL;DR: It is shown that a type of recurrent neural network which has feedback but no hidden state neurons can learn a special type of FSM called a finite memory machine (FMM) under certain constraints.
82
Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits
Aiman H. El-Maleh,Ahmad T. Sheikh,Sadiq M. Sait +2 more
- 01 Dec 2013
TL;DR: An improved binary particle swarm optimization (BPSO) algorithm is proposed and its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization is demonstrated.
73
References
Input Don''t Care Sequences
Huey Wang,Robert K. Brayton +1 more
- 01 Jan 1993
TL;DR: An approach to computer all input don''t care sequences for a component in an FSM network with an arbitrary topology and approximate methods for managing the complexity of large FSM networks are presented.
3