Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing

Author:

Kazemi Arman,Müller Franz,Sharifi Mohammad Mehdi,Errahmouni Hamza,Gerlach Gerald,Kämpfe Thomas,Imani Mohsen,Hu Xiaobo Sharon,Niemier Michael

Abstract

AbstractHyperdimensional computing (HDC) is a brain-inspired computational framework that relies on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple manipulations of hypervectors and can be incredibly memory-intensive. In-memory computing (IMC) can greatly improve the efficiency of HDC by reducing data movement in the system. Most existing IMC implementations of HDC are limited to binary precision which inhibits the ability to match software-equivalent accuracies. Moreover, memory arrays used in IMC are restricted in size and cannot immediately support the direct associative search of large binary HVs (a ubiquitous operation, often over 10,000+ dimensions) required to achieve acceptable accuracies. We present a multi-bit IMC system for HDC using ferroelectric field-effect transistors (FeFETs) that simultaneously achieves software-equivalent-accuracies, reduces the dimensionality of the HDC system, and improves energy consumption by 826x and latency by 30x when compared to a GPU baseline. Furthermore, for the first time, we experimentally demonstrate multi-bit, array-level content-addressable memory (CAM) operations with FeFETs. We also present a scalable and efficient architecture based on CAMs which supports the associative search of large HVs. Furthermore, we study the effects of device, circuit, and architectural-level non-idealities on application-level accuracy with HDC.

Funder

Semiconductor Research Corporation

ECSEL Joint Undertaking project TEMPO in collaboration with the European Union’s H2020 Framework Program and National Authorities

National Science Foundation, United States

Office of Naval Research,United States

Air Force Office of Scientific Research

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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1. Temperature- and variability-aware compact modeling of ferroelectric FDSOI FET for memory and emerging applications;Solid-State Electronics;2024-08

2. A Computing-in-Memory-Based One-Class Hyperdimensional Computing Model for Outlier Detection;IEEE Transactions on Computers;2024-06

3. Low power nanoscale S-FED based single ended sense amplifier applied in integrate and fire neuron circuit;Scientific Reports;2024-05-09

4. C4CAM: A Compiler for CAM-based In-memory Accelerators;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3;2024-04-27

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