Open AccessProceedings Article
Software for ANN training on a Ring Array Processor
Phil Kohn,Jeff A. Bilmes,Nelson Morgan,James Beck +3 more
- 02 Dec 1991
- Vol. 4, pp 781-788
TL;DR: This report describes CLONES and shows how it is implemented on the RAP, an efficient and flexible realization of Connectionist Layered Object-oriented Network Simulator.
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Abstract: Experimental research on Artificial Neural Network (ANN) algorithms requires either writing variations on the same program or making one monolithic program with many parameters and options. By using an object-oriented library, the size of these experimental programs is reduced while making them easier to read, write and modify. An efficient and flexible realization of this idea is Connectionist Layered Object-oriented Network Simulator (CLONES). CLONES runs on UNIX workstations and on the 100-1000 MFLOP Ring Array Processor (RAP) that we built with ANN algorithms in mind. In this report we describe CLONES and show how it is implemented on the RAP.
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Citations
Fast neural net simulation with a DSP processor array
TL;DR: This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer that gives a computing performance to a single user which was unthinkable before.
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NSK, an object-oriented simulator kernel for arbitrary feedforward neural networks
Cedric Gegout,Bernard Girau,Fabrice Rossi +2 more
- 06 Nov 1994
TL;DR: An object-oriented neural network simulator kernel is presented, based on a general mathematical model for arbitrary feedforward nets, which satisfies the following requirements: expandability, portability and efficiency.
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•Proceedings Article
High Performance Neural Net Simulation on a Multiprocessor System with "Intelligent" Communication
U.A. Muller,Michael Kocheisen,Anton Gunzinger +2 more
- 29 Nov 1993
TL;DR: This paper describes the simulation of neural nets on the MUSIC parallel supercomputer, a system that shows a good balance between the three issues and therefore made many research projects possible that were unthinkable before.
Learning Topology-Preserving Maps Using Self-Supervised Backpropagation
Arnfried Ossen
- 13 Sep 1993
TL;DR: A simple extension of the cost function of backpropagation leads to a competitive version of self-supervised back Propagation, which can be used to produce topographic maps.
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References
The RAP: a ring array processor for layered network calculations
Nelson Morgan,James Beck,Philip D. Kohn,Jeff A. Bilmes,Eric Allman,J. Beer +5 more
- 05 Sep 1990
TL;DR: The authors have designed and implemented a ring array processor, RAP, for fast implementation of layered neural network algorithms, a multi-DSP system targeted at continuous speech recognition using connectionist algorithms.
35
SPERT: a VLIW/SIMD microprocessor for artificial neural network computations
Krste Asanovic,James Beck,Brian Kingsbury,Phil Kohn,Nelson Morgan,John Wawrzynek +5 more
- 04 Aug 1992
TL;DR: SPERT (synthetic perceptron testbed) is a fully programmable single chip microprocessor designed for efficient execution of artificial neural network algorithms that represents over an order of magnitude reduction in cost for problems where fixed-point arithmetic is satisfactory.
22
•Proceedings Article
Connectionist Approaches to the Use of Markov Models for Speech Recognition
Hervé Bourlard,Nelson Morgan,Chuck Wooters +2 more
- 01 Oct 1990
TL;DR: Results support the previously reported utility of MLP probability estimation for continuous speech recognition and are generalized to take account of time correlation between successive observations, without any restrictive assumptions about the driving noise.