Design And Analysis Of A Systolic Array For Neural Computation

Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.

Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.

Abstract: "As simulations of large neural networks on a sequential computer frequently require days and even weeks of computations, and the long computational time had been a critical obstacle for progress in neural network researches, extensive research efforts are being devoted to the parallel implementation of neural networks. A systolic array is one of the best solutions to these problems. It can overcome the communication problems generated by the highly interconnected neurons, and can exploit the massive parallelism inherent in the problem. Moreover since the computation of neural networks can be represented by a series of matrix-by-vector multiplications, the classical systolic algorithms can be used to implement them. There have been several research efforts on systolic algorithms and systolic array structures to implement neural networks. These approaches are classified into two groups. One is mapping the systolic algorithms for neural networks onto parallel computers such as Warp, MasPar MP-1, and Transputer arrays, and the other is designing a VLSI systolic array dedicated to specific models. In this paper, we investigate the systolic array implementations of neural networks. The basic systolic algorithms and the systolic array structures to implement the neural networks, the issues of exploiting the parallelisms inherent in the neural networks, and the various systolic approaches are studied."

Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial & Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed & selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation & implementation, image processing & engineering applications.

This book constitutes, together with its compagnion LNCS 1607, the refereed proceedings of the International Work-Conference on Artificial and Natural Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 89 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to foundational issues of neural computation and tools for neural modeling. The papers are organized in parts on neural modeling: biophysical and structural models; plasticity phenomena: maturing, learning, and memory; and artificial intelligence and cognitive neuroscience.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

Circuits and architectures have become more complex in terms of structure, interconnection topology, and data flow. Design correctness has become increasingly significant, as errors in design may result in strenuous debugging, or even in the repetition of a costly manufacturing process. Although circuit simulation has been used traditionally and widely as the technique for checking hardware and architectural designs, it does not guarantee the conformity of designs to specifications. Formal methods therefore become vital in guaranteeing the correctness of designs and have thus received a significant amount of attention in the CAD industry today.This book presents a formal method for specifying and verifying the correctness of systolic array designs. Such architectures are commonly found in the form of accelerators for digital signal, image, and video processing. These arrays can be quite complicated in topology and data flow. In the book, a formalism called STA is defined for these kinds of dynamic environments, with a survey of related techniques. A framework for specification and verification is established. Formal verification techniques to check the correctness of the systolic networks with respect to the algorithmic level specifications are explained. The book also presents a Prolog-based formal design verifier (named VSTA), developed to automate the verification process, as using a general purpose theorem prover is usually extremely time-consuming. Several application examples are included in the book to illustrate how formal techniques and the verifier can be used to automate proofs.

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.