A fully-integrated analog machine learning classifier for breast cancer classification

Sanjeev T. Chandrasekaran, Ruobing Hua, Imon Banerjee, Arindam Sanyal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 µW power and occupies 0.003 mm2 die area.

Original languageEnglish (US)
Article number515
JournalElectronics (Switzerland)
Volume9
Issue number3
DOIs
StatePublished - Mar 2020
Externally publishedYes

Keywords

  • Analog-AI
  • Breast cancer detection
  • Classification
  • CS amplifier
  • Intelligence-at-the-edge
  • Machine learning
  • Neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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