Multiple-function multi-input/multi-output digital control and on-line analysis

The design and capabilities of two digital controller systems for aeroelastic wind-tunnel models are described. The first allowed control of flutter while performing roll maneuvers with wing load control as well as coordinating the acquisition, storage, and transfer of data for on-line analysis. This system, which employs several digital signal multi-processor (DSP) boards programmed in high-level software languages, is housed in a SUN Workstation environment. A second DCS provides a measure of wind-tunnel safety by functioning as a trip system during testing in the case of high model dynamic response or in case the first DCS fails. The second DCS uses National Instruments LabVIEW Software and Hardware within a Macintosh environment

The use of field-programmable gate arrays for the hardware acceleration of design automation tasks

This paper investigates the possibility of using Field-Programmable Gate Arrays (Fr’GAS) as reconfigurable co-processors for workstations to produce moderate speedups for most tasks in the design process, resulting in a worthwhile overall design process speedup at low cost and allowing algorithm upgrades with no hardware modification. The use of FPGAS as hardware accelerators is reviewed and then achievable speedups are predicted for logic simulation and VLSI design rule checking tasks for various FPGA co-processor arrangements

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