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 (=3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3x3</sup> /2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3x3</sup> ) flexibility on 3x3 filters in convolutional neural networks (CNNs), and 1.585x bit density improvement, thereby enabling advanced CIM applications with ternary weight networks.
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