Energy-Efficient Deep In-memory Architecture for NAND Flash Memories

Sujan K. Gonugondla, Mingu Kang, Yongjune Kim, Mark Helm, Sean Eilert, Naresh Shanbhag

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

Abstract

This paper proposes an energy-efficient deep in-memory architecture for NAND flash (DIMA-F) to perform machine learning and inference algorithms on NAND flash memory. Algorithms for data analytics, inference, and decision-making require processing of large data volumes and are hence limited by data access costs. DIMA-F achieves energy savings and throughput improvement for such algorithms by reading and processing data in the analog domain at the periphery of NAND flash memory. This paper also provides behavioral models of DIMA-F that can be used for analysis and large scale system simulations in presence of circuit non-idealities and variations. DIMA-F is studied in the context of linear support vector machines and k-nearest neighbor for face detection and recognition, respectively. An estimated 8×-to-23× reduction in energy and 9×-to-15× improvement in throughput resulting in EDP gains up to 345× over the conventional NAND flash architecture incorporating an external digital ASIC for computation.

Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648810
DOIs
StatePublished - Apr 26 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: May 27 2018May 30 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2018-May
ISSN (Print)0271-4310

Other

Other2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/TerritoryItaly
CityFlorence
Period5/27/185/30/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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