A FerroFET based in-memory processor for solving distributed and iterative optimizations via least-squares method

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

Research output: Contribution to journal › Article

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 an SRAM based Processing-In-Memory (PIM) architecture.

Original languageEnglish (US)
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
DOIs
StatePublished - Jan 1 2019

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Data storage equipment
Processing
Memory architecture
Static random access storage
Field effect transistors
Ferroelectric materials
Energy efficiency
Learning systems

All Science Journal Classification (ASJC) codes

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

Cite this

Yoon, Insik ; Chang, Muya ; Ni, Kai ; Jerry, Matthew ; Gangopadhyay, Samantak ; Smith, Gus Henry ; Hamam, Tomer ; Romberg, Justin ; Narayanan, Vijaykrishnan ; Khan, Asif ; Datta, Suman ; Raychowdhury, Arijit. / A FerroFET based in-memory processor for solving distributed and iterative optimizations via least-squares method. In: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 2019.
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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 an SRAM based Processing-In-Memory (PIM) architecture.",
author = "Insik Yoon and Muya Chang and Kai Ni and Matthew Jerry and Samantak Gangopadhyay and Smith, {Gus Henry} and Tomer Hamam and Justin Romberg and Vijaykrishnan Narayanan and Asif Khan and Suman Datta and Arijit Raychowdhury",
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doi = "10.1109/JXCDC.2019.2930222",
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Yoon, I, Chang, M, Ni, K, Jerry, M, Gangopadhyay, S, Smith, GH, Hamam, T, Romberg, J, Narayanan, V, Khan, A, Datta, S & Raychowdhury, A 2019, 'A FerroFET based in-memory processor for solving distributed and iterative optimizations via least-squares method', IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. https://doi.org/10.1109/JXCDC.2019.2930222

A FerroFET based in-memory processor for solving distributed and iterative optimizations via least-squares method. / Yoon, Insik; Chang, Muya; Ni, Kai; Jerry, Matthew; Gangopadhyay, Samantak; Smith, Gus Henry; Hamam, Tomer; Romberg, Justin; Narayanan, Vijaykrishnan; Khan, Asif; Datta, Suman; Raychowdhury, Arijit.

In: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 01.01.2019.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A FerroFET based in-memory processor for solving distributed and iterative optimizations via least-squares method

AU - Yoon, Insik

AU - Chang, Muya

AU - Ni, Kai

AU - Jerry, Matthew

AU - Gangopadhyay, Samantak

AU - Smith, Gus Henry

AU - Hamam, Tomer

AU - Romberg, Justin

AU - Narayanan, Vijaykrishnan

AU - Khan, Asif

AU - Datta, Suman

AU - Raychowdhury, Arijit

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 an SRAM based Processing-In-Memory (PIM) architecture.

AB - 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 an SRAM based Processing-In-Memory (PIM) architecture.

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Yoon I, Chang M, Ni K, Jerry M, Gangopadhyay S, Smith GH et al. A FerroFET based in-memory processor for solving distributed and iterative optimizations via least-squares method. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 2019 Jan 1. https://doi.org/10.1109/JXCDC.2019.2930222

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