Sakhuja, JayatikaJayatikaSakhujaLashkare, SandipSandipLashkareGanguly, UdayanUdayanGanguly2025-08-312025-08-312024-01-01[9798350373738]10.1109/DRC61706.2024.106054672-s2.0-85201048319https://d8.irins.org/handle/IITG2025/29204Vector-Matrix-Multiplication (VMM) via multiply and accumulate operation (MAC) is essential in computations encompassing neuromorphic and deep learning applications (Fig. 1a) [1]. The research has been focused on emerging non-volatile memories (NVMs) with resistive random-access memories (RRAM) as a leading candidate for a viable alternate technology [2]. In crossbar arrays, the currents through the columns/bit lines follow KCL and Ohm's law, resulting in MAC, thereby reducing computational complexity (Fig. 1b) [3]. However, given the device's non-idealities, it poses challenges in achieving accuracy levels. The accumulated current collected at the bit line is susceptible to bit-cell variability (I var), a finite current ratio (k) , and the current contribution from the 'off' state (high resistance state-I<inf>HRS</inf>) (Fig. 1c) [4]. This work emphasizes the importance of a device-aware quantization scheme, i.e., considering device non-idealities at MAC outputs. We analyze the contribution of different non-idealities in defining the quantization scheme using Pr<inf>1-x</inf> Ca<inf>x</inf>MnO<inf>3</inf> (PCMO) based RRAM arrays. Using non-uniform quantization, we show a successful VMM via MAC operation in PCMO-RRAM arrays. Further, we show how non-uniform quantization for non-ideal current can facilitate (2x) the size of the array compared to uniform quantization. While non-uniform quantization allows for a larger array, the constraints by tolerable device variability can be stringent and limit the array size. For an array size (n) of 4 and a current ratio (k) of 5, the estimated tolerable I var is less than 0.2I HRS.falseConference Paper20241cpConference Proceeding0