A Generalized Stochastic Implementation of the Disparity Energy Model for Depth Perception
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Implementing neuromorphic algorithms is increasingly interesting as the error resilience and low-area, low-energy nature of biological systems becomes the potential solution for problems in robotics and artificial intelligence. While conventional digital methods are inefficient in implementing massively parallel systems, analog solutions are hard to design and program. Stochastic Computing (SC) is a natural bridge that allows pseudo-analog computations in the digital domain using low complexity hardware. However, large scale SC systems traditionally suffered from long latencies, hence higher energy consumption. This work develops a VLSI architecture for an SC based binocular vision system based on a disparity-energy model that emulates the hierarchical multi-layered neural structure in the primary visual cortex. The 3-layer neural network architecture is biologically plausible and is tuned to detecting 5 different disparities. The architecture is compact, adder-free, and achieves better disparity detection compared to a floating-point version by using a modified disparity-energy model. A generalized 1x100 pixel processing system is synthesized using TSMC 65nm CMOS technology and it achieves 71 % reduction in area-delay product and 48 % in energy savings compared to a fixed-point implementation at equivalent precision.
KeywordsStochastic computing Neuromorphic computing Approximate computing Gabor filters Disparity-energy model Computer vision Biomedical electronics Neural networks
The authors would like to thank Hasan Mozafari, Arash Ardakani and Xinchi Chen for useful discussions. Warren J. Gross is a member of ReSMiQ (Regroupement Stratégique en Microsystémes du Québec) and SYTACom (Centre de recherche sur les systèmes et les technologies avancés en communications). This work was supported by the Brainware LSI Project of MEXT (Ministry of education, culture, sports, science and technology), Japan.
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