Gabor filter based on stochastic computation

Naoya Onizawa, Daisaku Katagiri, Kazumichi Matsumiya, Warren J. Gross, Takahiro Hanyu

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


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\hbox{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.

Original languageEnglish
Article number7010006
Pages (from-to)1224-1228
Number of pages5
JournalIEEE Signal Processing Letters
Issue number9
Publication statusPublished - 2015 Sept 1


  • Digital circuit implementation
  • stochastic computing


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