Exploiting FeFETs via Cross-Layer Design from In-memory Computing Circuits to Meta-Learning Applications
Dayane Reis1,a, Ann Franchesca Laguna1,b, Michael Niemier2,a and Xiaobo Sharon Hu3
Department of Computer Science and Engineering University of Notre Dame, Notre Dame, IN, USA, 46556
adreis@nd.edu
balaguna@nd.edu
cmniemier@nd.edu
dshu@nd.edu
ABSTRACT
A ferroelectric FET (FeFET), made by integrating a ferroelectric material layer in the gate stack of a MOSFET, is a device that can behave as both a transistor and a non-volatile storage element. This unique property of FeFETs enables area efficient and low-power merged logic and memory functionality, desirable for many data analytic and machine learning applications. To best exploit this unique feature of FeFETs, crosslayer design practices spanning from circuits and architectures to algorithms and applications is needed. The paper presents FeFETbased circuits and architectures that offer, either independently or in a configurable fashion, content addressable memory (TCAM) and general-purpose compute-in-memory (GP-CiM) functionalities. These in-memory computing modules bring new opportunities to accelerating data-intensive applications. We discuss the use of these FeFET based in-memory computing fabrics in meta-learning applications, specifically as attentional memory. System-level task mapping and end-to-end evaluation will be discussed.
Keywords: In-Memory Computing, Ternary Contentaddressable Memory, CiM, TCAM, FeFET, Meta-Learning.
