Implementing the new Zero Instruction Set Computer (ZISC036) from IBM for a Higgs search

Lindblad, Th.; Lindsey, Clark S.; Minerskjöld, Maxim; Sekhniaidze, G.; Székely, G.; Eide, Åge

doi:10.1016/0168-9002(95)00074-7

Cited by 10 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The efficiency of the ZISC in real-time vision or pattern recognition systems was thereafter recognized and many other practical applications followed. • Lindblad et al [43] used the ZISC to look for Higg's boson events amongst the very large number of traces made by the elementary particles created in high energy particle accelerators. Lindsey et al [45], and Minerskjöld [45] also report tests of the ZISC used in high energy physics tasks.…”

Section: The Zisc and The Cognimemmentioning

confidence: 99%

Infrared target-flare discrimination using a ZISC hardware neural network

Labonté

Deck

2009

J Real-Time Image Proc

View full text Add to dashboard Cite

The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile's field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.

show abstract

Section: The Zisc and The Cognimemmentioning

confidence: 99%

Infrared target-flare discrimination using a ZISC hardware neural network

Labonté

Deck

2009

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…Industry has also developed an interest in non-von Neumann architectures for computing applications. The CM1K chip from CogniMem (Cognimem Technologies, Inc. 2013) was related to the IBM ZISC036 technology (Eide et al, 1994) and Intel Corporation's radial basis function (RBF) effort (Holler et al, 1992). The CM1K chip was a fully parallel chip with 1024 silicon neurons that used either a RBF or K-nearest neighbor non-linear classifier to learn patterns up to 256 bytes.…”

Section: Resurgence In Artificial Neural Network and Neuromorphic Commentioning

confidence: 99%

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

James

Aimone

Miner

et al. 2017

Biologically Inspired Cognitive Architectures

View full text Add to dashboard Cite

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and others were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the

show abstract

“…Hardware implementations of the RBF approach have be realized for different applications, on either FPGA [26], or neurochip [27]. Commercial RBF products include the IBM ZISC chip and the Nestor Ni 1000 chip [28]. Here, our aim is to elaborate in real time an efficient model of unconstrained face tracking and identity verification in arbitrary scenes.…”

Section: Hardware Implementationsmentioning

confidence: 99%

“…We also made hardware implementation of our model using a commercial board linked to pattern recognition applications. This General Vision Neurosight board [30] contains a CMOS sensor (288 352 pixels), an FPGA Xilinx SpartanII-50, two memory banks of 512KB each, as well as two specific ZISC chips [28] (see Fig. 15).…”

Section: B Second Realization: Implementation Based On Zisc Chip 1) mentioning

confidence: 99%

Implementation of an rbf neural network on embedded systems: real-time face tracking and identity verification

Yang

Paindavoine

2003

IEEE Trans. Neural Netw.

143

View full text Add to dashboard Cite

This paper describes a real time vision system that allows us to localize faces in video sequences and verify their identity. These processes are image processing techniques based on the radial basis function (RBF) neural network approach. The robustness of this system has been evaluated quantitatively on eight video sequences. We have adapted our model for an application of face recognition using the Olivetti Research Laboratory (ORL), Cambridge, UK, database so as to compare the performance against other systems. We also describe three hardware implementations of our model on embedded systems based on the field programmable gate array (FPGA), zero instruction set computer (ZISC) chips, and digital signal processor (DSP) TMS320C62, respectively. We analyze the algorithm complexity and present results of hardware implementations in terms of the resources used and processing speed. The success rates of face tracking and identity verification are 92% (FPGA), 85% (ZISC), and 98.2% (DSP), respectively. For the three embedded systems, the processing speeds for images size of 288 /spl times/ 352 are 14 images/s, 25 images/s, and 4.8 images/s, respectively.

show abstract

Implementing the new Zero Instruction Set Computer (ZISC036) from IBM for a Higgs search

Cited by 10 publications

References 4 publications

Infrared target-flare discrimination using a ZISC hardware neural network

Infrared target-flare discrimination using a ZISC hardware neural network

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

Implementation of an rbf neural network on embedded systems: real-time face tracking and identity verification

Contact Info

Product

Resources

About