Abstract
RRAM is a promising candidate for compute-in-memory (CIM) applications owing to its natural multiply-and-accumulate (MAC)-supporting structure, high bit-density, non-volatility, and a monolithic CMOS and RRAM process. In particular, multi-bit encoding in RRAM cells helps support advanced applications such as AI with higher MAC throughput and bit-density. Notwithstanding prior efforts into commercializing RRAM technology, underlying challenges hinder the wide usage of RRAM [1]. As a circuit-domain approach to address the challenges, this paper presents a 101.4Kb ternary-weight RRAM macro with 256x256 cells supporting: (1) CIM for ternary weight networks by employing voltage-based read (RD) with active feedback surmounting a low resistance ratio (R-ratio) between the high resistance state (HRS) and the low resistance state (LRS) in high-endurance RRAM, and (2) iterative write with verification (IWR) to facilitate a reliable multi-bit encoding under a narrow margin. Compared to [2] supporting CIM with binary RRAM cells, this work provides 38.44x (=33x3/23x3) flexibility on 3x3 filters in convolutional neural networks (CNNs), and 1.585x bit density improvement, thereby enabling advanced CIM applications with ternary weight networks.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE Custom Integrated Circuits Conference, CICC 2021 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728175812 |
| DOIs | |
| State | Published - Apr 2021 |
| Event | 2021 IEEE Custom Integrated Circuits Conference, CICC 2021 - Virtual, Austin, United States Duration: 25 Apr 2021 → 30 Apr 2021 |
Publication series
| Name | Proceedings of the Custom Integrated Circuits Conference |
|---|---|
| Volume | 2021-April |
| ISSN (Print) | 0886-5930 |
Conference
| Conference | 2021 IEEE Custom Integrated Circuits Conference, CICC 2021 |
|---|---|
| Country/Territory | United States |
| City | Virtual, Austin |
| Period | 25/04/21 → 30/04/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
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