Ultra high speed image processing techniques

Packaging techniques for ultra high speed image processing were developed. These techniques involve the development of a signal feedthrough technique through LSI/VLSI sapphire substrates. This allows the stacking of LSI/VLSI circuit substrates in a 3 dimensional package with greatly reduced length of interconnecting lines between the LSI/VLSI circuits. The reduced parasitic capacitances results in higher LSI/VLSI computational speeds at significantly reduced power consumption levels

Architecture for VLSI design of Reed-Solomon encoders

The logic structure of a universal VLSI chip called the symbol-slice Reed-Solomon (RS) encoder chip is discussed. An RS encoder can be constructed by cascading and properly interconnecting a group of such VLSI chips. As a design example, it is shown that a (255,223) RD encoder requiring around 40 discrete CMOS ICs may be replaced by an RS encoder consisting of four identical interconnected VLSI RS encoder chips. Besides the size advantage, the VLSI RS encoder also has the potential advantages of requiring less power and having a higher reliability

A knowledge-based approach to VLSI-design in an open CAD-environment

A knowledge-based approach is suggested to assist a designer in the increasingly complex task of generating VLSI-chips from abstract, high-level specifications of the system. The complexity of designing VLSI-circuits has reached a level where computer-based assistance has become indispensable. Not all of the design tasks allow for algorithmic solutions. AI technique can be used, in order to support the designer with computer-aided tools for tasks not suited for algorithmic approaches. The approach described in this paper is based upon the underlying characteristics of VLSI design processes in general, comprising all stages of the design. A universal model is presented, accompanied with a recording method for the acquisition of design knowledge - strategic and task-specific - in terms of the design actions involved and their effects on the design itself. This method is illustrated by a simple design example: the implementation of the logical EXOR-component. Finally suggestions are made for obtaining a universally usable architecture of a knowledge-based system for VLSI-design

Hybrid-aligned nematic liquid-crystal modulators fabricated on VLSI circuits

A new method for fabricating analog light modulators on VLSI devices is described. The process is fully compatible with devices fabricated by commercial VLSI foundries, and the assembly of the modulator structures requires a small number of simple processing steps. The modulators are capable of analog amplitude or phase modulation and can operate at video rates and at low voltages (2.2 V). The modulation mechanism and the process yielding the modulator structures are described. Experimental data are presented

Parallel VLSI architecture emulation and the organization of APSA/MPP

The Applicative Programming System Architecture (APSA) combines an applicative language interpreter with a novel parallel computer architecture that is well suited for Very Large Scale Integration (VLSI) implementation. The Massively Parallel Processor (MPP) can simulate VLSI circuits by allocating one processing element in its square array to an area on a square VLSI chip. As long as there are not too many long data paths, the MPP can simulate a VLSI clock cycle very rapidly. The APSA circuit contains a binary tree with a few long paths and many short ones. A skewed H-tree layout allows every processing element to simulate a leaf cell and up to four tree nodes, with no loss in parallelism. Emulation of a key APSA algorithm on the MPP resulted in performance 16,000 times faster than a Vax. This speed will make it possible for the APSA language interpreter to run fast enough to support research in parallel list processing algorithms

Neuromorphic analogue VLSI

Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays

Sheik S, Chicca E, Indiveri G. Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays. Presented at the International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.Axonal delays are used in neural computation to implement faithful models of biological neural systems, and in spiking neural networks models to solve computationally demanding tasks. While there is an increasing number of software simulations of spiking neural networks that make use of axonal delays, only a small fraction of currently existing hardware neuromorphic systems supports them. In this paper we demonstrate a strategy to implement temporal delays in hardware spiking neural networks distributed across multiple Very Large Scale Integration (VLSI) chips. This is achieved by exploiting the inherent device mismatch present in the analog circuits that implement silicon neurons and synapses inside the chips, and the digital communication infrastructure used to configure the network topology and transmit the spikes across chips. We present an example of a recurrent VLSI spiking neural network that employs axonal delays and demonstrate how the proposed strategy efficiently implements them in hardware

Memetic Multilevel Hypergraph Partitioning

Hypergraph partitioning has a wide range of important applications such as VLSI design or scientific computing. With focus on solution quality, we develop the first multilevel memetic algorithm to tackle the problem. Key components of our contribution are new effective multilevel recombination and mutation operations that provide a large amount of diversity. We perform a wide range of experiments on a benchmark set containing instances from application areas such VLSI, SAT solving, social networks, and scientific computing. Compared to the state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our new algorithm computes the best result on almost all instances