Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference
  • OSA Technical Digest (Optica Publishing Group, 2019),
  • paper jsi_1_1

In-memory computing using electrical and photonic memory devices

Not Accessible

Your library or personal account may give you access

Abstract

Given the explosive growth in data-centric artificial intelligence workloads and the imminent end of CMOS scaling laws, it is becoming increasingly clear that we need to transition to brain-inspired architectures where memory and processing are collocated. A first step in this direction could be in-memory computing whereby certain computational tasks are performed in place by exploiting the physical attributes of memory devices [1,2]. In the conventional electrical domain, emerging resistive memory devices or memristive devices could play a key role as elements of such a computational memory unit. For example, when organized in a cross-bar configuration, phase-change memory (PCM) devices can be used to perform matrix-vector multiplications with very low computational complexity [3]. An appealing application of this concept is for the problem of compressed sensing and recovery of high-dimensional signals [4]. This is an application in which the lack of precision arising from the matrix-vector multiplication operations is not prohibitive. However, in other applications such as solving systems of linear equations or training deep neural networks, the lack of precision could be a key challenge. To address this, the concept of mixed-precision in-memory computing was proposed, where through a judicious combination of high-precision processing units and computational memory, we can achieve arbitrarily high precision, while still retaining much of the benefits of non-von Neumann computing [5].

© 2019 IEEE

PDF Article
More Like This
All-photonic in-memory computing based on phase-change materials

Carlos Ríos, Nathan Youngblood, Zengguang Cheng, Manuel Le Gallo, Wolfram H.P. Pernice, C. David Wright, Abu Sebastian, and Harish Bhaskaran
SM2J.2 CLEO: Science and Innovations (CLEO:S&I) 2019

Neuro-inspired Computing: From Resistive Memory to Optics

Charles Mackin, Pritish Narayanan, Hsinyu Tsai, Stefano Ambrogio, An Chen, and Geoffrey W. Burr
ce_3_3 The European Conference on Lasers and Electro-Optics (CLEO/Europe) 2019

Intelligent Computing with Photonic Memories

Mario Miscuglio, Omer Yesiliurt, Jiawei Meng, Ludmila J. Prokopeva, Yifei Zhang, Armin Mehrabian, Juejun Hu, Alexander V. Kildishev, and Volker J. Sorger
W3A.4 Optical Fiber Communication Conference (OFC) 2020

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved