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In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories

Authors :
Kazemi, Arman
Sharifi, Mohammad Mehdi
Laguna, Ann Franchesca
Müller, Franz
Rajaei, Ramin
Olivo, Ricardo
Kämpfe, Thomas
Niemier, Michael
Hu, X. Sharon
Kazemi, Arman
Sharifi, Mohammad Mehdi
Laguna, Ann Franchesca
Müller, Franz
Rajaei, Ramin
Olivo, Ricardo
Kämpfe, Thomas
Niemier, Michael
Hu, X. Sharon
Publication Year :
2020

Abstract

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing $L_\infty$ and Hamming distance metrics, but they cannot achieve software-comparable accuracies. This paper proposes a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (FeFETs) to perform a single-step, in-memory NN search. Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks. As an example, the proposed method achieves a 98.34% accuracy for a 5-way, 5-shot classification task for the Omniglot dataset (only 0.8% lower than software-based implementations) with a 3-bit MCAM. This represents a 13% accuracy improvement over state-of-the-art TCAM-based implementations at iso-energy and iso-delay. The presented distance function is resilient to the effects of FeFET device-to-device variations. Furthermore, this work experimentally demonstrates a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate proof of concept.<br />Comment: To be published in DATE'21

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1228446414
Document Type :
Electronic Resource