Continuous-valued probabilistic behavior in a VLSI generative model.
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.
Chen
Hsin
H
Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu 30055, Taiwan. hchen@ee.nthu.edu.tw
Fleury
Patrice C D
PC
Murray
Alan F
AF
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012666:Semiconductors; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*