A heterogeneous computer vision architecture: implementation issues

The prototype of a heterogeneous architecture is currently being built. The architecture is aimed at video-rate computing and is based on a message passing MIMD topology at the top level-transputer based-and on VLSI associative processor arrays (APA, SIMD structure) for low level image processing tasks. The APA structure is implemented through a set of 4 VLSI chips (GLiTCH) containing 64 1-bit processing elements each. This communication addresses some issues concerning the implementation of the first prototype, namely those related to: • the design and integration of the APA controller unit, which provides the required interface between the APA, the MIMD topology and the video image interface: • the evaluation of the GLiTCH chip through an emulator based on transputers and fast programmable devices; the emulator was designed to be flexible enough to evaluate later modifications to the GLiTCH design; • the design of an integrated set of software development tools containing a structured editor-syntax oriented, with a visual interface/programming interface-and a cross compiler and debugger

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Towards the Teraflop CFD

We are surveying current projects in the area of parallel supercomputers. The machines considered here will become commercially available in the 1990 - 1992 time frame. All are suitable for exploring the critical issues in applying parallel processors to large scale scientific computations, in particular CFD calculations. This chapter presents an overview of the surveyed machines, and a detailed analysis of the various architectural and technology approaches taken. Particular emphasis is placed on the feasibility of a Teraflops capability following the paths proposed by various developers

A 2-D PROCESSOR ARRAY FOR MASSIVELY PARALLEL IMAGE PROCESSING

The concept of introducing image processing logic within the spatial gaps of an array of photodiodes is the key factor behind the presented work. A two-dimensional massively parallel image processing paradigm based on 8X8 pixel neighborhood digital processors has been designed. A low complexity processor array architecture along with its instruction set has been designed and fully verified on a FPGA platform. Various image processing tests have been run on the FPGA platform to demonstrate the functionality of a design that uses 12 parallel processors. The test results indicate that the architecture is scalable to support high frame rates while allowing for flexible processing due to inherent programmability at a high level. The gate level logic synthesis results of the processor targeting a 0.13 μm CMOS technology indicates a low silicon area complexity, allowing for image sensor integration. Adviser: Dr. Sina Balkir, Co-Adviser: Dr. Michael W. Hoffma

Algorithms and programming tools for image processing on the MPP, part 2

A number of algorithms were developed for image warping and pyramid image filtering. Techniques were investigated for the parallel processing of a large number of independent irregular shaped regions on the MPP. In addition some utilities for dealing with very long vectors and for sorting were developed. Documentation pages for the algorithms which are available for distribution are given. The performance of the MPP for a number of basic data manipulations was determined. From these results it is possible to predict the efficiency of the MPP for a number of algorithms and applications. The Parallel Pascal development system, which is a portable programming environment for the MPP, was improved and better documentation including a tutorial was written. This environment allows programs for the MPP to be developed on any conventional computer system; it consists of a set of system programs and a library of general purpose Parallel Pascal functions. The algorithms were tested on the MPP and a presentation on the development system was made to the MPP users group. The UNIX version of the Parallel Pascal System was distributed to a number of new sites