GDOT: A graphene-based nanofunction for dot-product computation

Ning C. Wang, Sujan K. Gonugondla, Ihab Nahlus, Naresh R. Shanbhag, Eric Pop

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Though much excitement surrounds two-dimensional (2D) beyond CMOS fabrics like graphene and MoS2, most efforts have focused on individual devices, with few high-level implementations. Here we present the first graphene-based dot-product nanofunction (GDOT) using a mixed-signal architecture. Dot product kernels are essential for emerging image processing and neuromorphic computing applications, where energy efficiency is prioritized. SPICE simulations of GDOT implementing a Gaussian blur show up to ∼104 greater signal-To-noise ratio (SNR) over CMOS based implementations-a direct result of higher graphene mobility in a circuit tolerant to low on/off ratios. Energy consumption is nearly equivalent, implying the GDOT can operate faster at higher SNR than CMOS counter-parts while preserving energy benefits over digital implementations. We implement a prototype 2-input GDOT on a wafer-scale 4″ process, with measured results confirming dot-product operation and lower than expected computation error.

Original languageEnglish (US)
Title of host publication2016 IEEE Symposium on VLSI Technology, VLSI Technology 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006373
DOIs
StatePublished - Sep 21 2016
Externally publishedYes
Event36th IEEE Symposium on VLSI Technology, VLSI Technology 2016 - Honolulu, United States
Duration: Jun 13 2016Jun 16 2016

Publication series

NameDigest of Technical Papers - Symposium on VLSI Technology
Volume2016-September
ISSN (Print)0743-1562

Other

Other36th IEEE Symposium on VLSI Technology, VLSI Technology 2016
Country/TerritoryUnited States
CityHonolulu
Period6/13/166/16/16

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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