A FerroFET-Based In-Memory Processor for Solving Distributed and Iterative Optimizations via Least-Squares Method

Insik Yoon, Asif Khan, Suman Datta, Arijit Raychowdhury, Muya Chang, Kai Ni, Matthew Jerry, Samantak Gangopadhyay, Gus Henry Smith, Tomer Hamam, Justin Romberg, Vijaykrishnan Narayanan

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In recent years, several designs that use in-memory processing to accelerate machine-learning inference problems have been proposed. Such designs are also a perfect fit for discrete, dynamic, and distributed systems that can solve large-dimensional optimization problems using iterative algorithms. For in-memory computations, ferroelectric field-effect transistors (FerroFETs) owing to their compact area and distinguishable multiple states offer promising possibilities. We present a distributed architecture that uses FerroFET memory and implements in-memory processing to solve a template problem of least squares minimization. Through this architecture, we demonstrate an improvement of 21 × in energy efficiency and 3 × in compute time compared to a static random access memory (SRAM)-based processing-in-memory (PIM) architecture.

Original languageEnglish (US)
Article number8767985
Pages (from-to)132-141
Number of pages10
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Volume5
Issue number2
DOIs
StatePublished - Dec 2019

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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