Continuous-valued probabilistic behavior in a VLSI generative model.

Hsin Chen; Patrice C D Fleury; Alan F Murray

doi:10.1109/tnn.2006.873278

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Continuous-valued probabilistic behavior in a VLSI generative model.

Hsin Chen·Patrice C D Fleury·Alan F Murray

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Abstract

This paper presents the VLSI implementation of the continuous restricted Boltzmann machine (CRBM), a probabilistic generative model that is able to model continuous-valued data with a simple and hardware-amenable training algorithm. The full CRBM system consists of stochastic neurons whose continuous-valued probabilistic behavior is mediated by injected noise. Integrating on-chip training circuits, the full CRBM system provides a platform for exploring computation with continuous-valued probabilistic behavior in VLSI. The VLSI CRBM's ability both to model and to regenerate continuous-valued data distributions is examined and limitations on its performance are highlighted and discussed.

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Algorithms Artificial Intelligence Computer Simulation Equipment Design Equipment Failure Analysis Models, Statistical Neural Networks, Computer Pattern Recognition, Automated Semiconductors Signal Processing, Computer-Assisted Evaluation Study Journal Article

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Hsin Chen, Patrice C D Fleury, Alan F Murray. 2006. Continuous-valued probabilistic behavior in a VLSI generative model.. https://doi.org/10.1109/tnn.2006.873278

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