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Gabor Filter Based on Stochastic Computation
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DOI：10.1109/LSP.2015.2392123.png)
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This letter introduces a design and proof-of-concept implementation of Gabor filters based on stochastic computation for area-efficient hardware. The Gabor filter exhibits a powerful image feature extraction capability, but it requires significant computational power. Using stochastic computation, a sine function used in the Gabor filter is approximated by exploiting several stochastic tanh functions designed based on a state machine. A stochastic Gabor filter realized using the stochastic sine shaper and a stochastic exponential function is simulated and compared with the original Gabor filter that shows almost equivalent behaviour at various frequencies and variance. A root-mean-square error of 0.043 at most is observed. In order to reduce long latency due to stochastic computation, 68 parallel stochastic Gabor filters are implemented in Silterra 0.13 mu m CMOS technology. As a result, the proposed Gabor filters achieve a 78% area reduction compared with a conventional Gabor filter while maintaining the comparable speed.
Keyword:
Digital circuit implementation
stochastic computing
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9.6
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1.1W
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1.7W
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