VLSI for Artificial Intelligence / Edition 1

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Table of Contents

1 Prolog Machines.- 1.1 From Low Level Semantic Description of Prolog to Instruction Set and VLSI Design.- 1.2 A 32 Bit Processor for Compiled Prolog.- 1.3 CARMEL-1: A VLSI Architecture for Flat Concurrent Prolog.- 1.4 VLSI for Parallel Execution of Prolog.- 2 Functional Programming Oriented Architectures.- 2.1 Supporting Functional and Logic Programming Languages through a Data Parallel VLSI Architecture.- 2.2 Translating Declaratively Specified Knowledge and Usage Requirements into a Reconfigurable Machine.- 3 Garbage Collection.- 3.1 VLSI-Appropriate Garbage Collection Support.- 3.2 A Self-timed Circuit for a Prolog Machine.- 4 Content-Addressable Memory.- 4.1 VLSI and Rule-Based Systems.- 4.2 Unify with Active Memory.- 4.3 The Pattern Addressable Memory: Hardware for Associative Processing.- 5 Knowledge Based Systems.- 5.1 A High Performance Relational Algebraic Processor for Large Knowledge Bases.- 5.2 A WSI Semantic Network Architecture.- 6 Neural Architectures.- 6.1 A VLSI Implementation of Multilayered Neural Networks.- 6.2 A Fully Digital Integrated CMOS Hopfield Network Including the Learning Algorithm.- 6.3 A Neural Network for 3-D VLSI Accelerator.- 6.4 Shift Invariant Associative Memory.- 7 Digital and Analog VLSI Neural Networks.- 7.1 VLSI Bit-Serial Neural Networks.- 7.2 A New CMOS Architecture for Neural Networks.- 7.3 A Limited-Interconnect, Highly Layered Synthetic Neural Architecture.- 7.4 VLSI-Design of Associative Networks.- 7.5 Fully-Programmable Analogue VLSI Devices for the Implementation of Neural Networks.- 8 Architectures for Neural Computing.- 8.1 Are Special Chips Necessary for Neural Computing?.- 8.2 A VLSI Systolic Array Dedicated to Hopfield Neural Network.- 8.3 An Integrated System for Neural Network Simulations.

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