Analog-to-stochastic converter using magnetic tunnel junction devices for vision chips

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

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper introduces an analog-to-stochastic converter using a magnetic tunnel junction (MTJ) device for vision chips based on stochastic computation. Stochastic computation has been recently exploited for area-efficient hardware implementation, such as low-density parity-check decoders and image processors. However, power-and-area hungry two-step (analog-to-digital and digital-to-stochastic) converters are required for the analog to stochastic signal conversion. To realize a one-step conversion, an MTJ device is used as it inherently exhibits a probabilistic switching behavior between two resistance states. Exploiting the device-based probabilistic behavior, analog signals can be directly and area efficiently converted to stochastic signals to mitigate the signal-conversion overhead. The analog-to-stochastic signal conversion is theoretically described and the conversion characteristic is evaluated using device and circuit parameters. In addition, the resistance variability of the MTJ device is considered in order to compensate the variability effect on the signal conversion. Based on the theoretical analysis, the analog-to-stochastic converter is designed in 90-nm CMOS and 100-nm MTJ technologies and is verified using a SPICE simulator (NS-SPICE) that handles both transistors and MTJ devices.

Original languageEnglish
Article number7362208
Pages (from-to)705-714
Number of pages10
JournalIEEE Transactions on Nanotechnology
Volume15
Issue number5
DOIs
Publication statusPublished - 2016 Sep

Keywords

  • Cognitive system
  • STT-MTJ
  • feature extraction
  • image sensor
  • signal conversion
  • stochastic computation

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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