In-Memory Computation of a Machine-Learning Classifier in a Standard 6T SRAM Array

Jintao Zhang, Zhuo Wang, Naveen Verma

Research output: Contribution to journalArticlepeer-review

337 Scopus citations


This paper presents a machine-learning classifier where computations are performed in a standard 6T SRAM array, which stores the machine-learning model. Peripheral circuits implement mixed-signal weak classifiers via columns of the SRAM, and a training algorithm enables a strong classifier through boosting and also overcomes circuit nonidealities, by combining multiple columns. A prototype 128 × 128 SRAM array, implemented in a 130-nm CMOS process, demonstrates ten-way classification of MNIST images (using image-pixel features downsampled from 28 × 28 = 784 to 9 × 9 = 81, which yields a baseline accuracy of 90%). In SRAM mode (bit-cell read/write), the prototype operates up to 300 MHz, and in classify mode, it operates at 50 MHz, generating a classification every cycle. With accuracy equivalent to a discrete SRAM/digital-MAC system, the system achieves ten-way classification at an energy of 630 pJ per decision, 113 times lower than a discrete system with standard training algorithm and 13 times lower than a discrete system with the proposed training algorithm.

Original languageEnglish (US)
Article number7875410
Pages (from-to)915-924
Number of pages10
JournalIEEE Journal of Solid-State Circuits
Issue number4
StatePublished - Apr 2017

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Analog computation
  • image detection
  • in-memory computation


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