Analog AI as a Service: A Cloud Platform for In-Memory Computing
Kaoutar El Maghraoui, Kim Tran, et al.
SSE 2024
Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here, it is shown that there is an additional non-ideality that can impact computational precision, namely the bias-polarity-dependent current flow. Using phase-change memory (PCM) as a model system, it is shown that this “current–voltage” non-ideality arises both from the material and geometrical properties of the devices. Further, we discuss the detrimental effects of such bipolar asymmetry on in-memory matrix-vector multiply (MVM) operations and provide a scheme to compensate for it.
Kaoutar El Maghraoui, Kim Tran, et al.
SSE 2024
Elena Ferro, A. Vasilopoulos, et al.
ISCAS 2024
Julian Büchel, William Simon, et al.
NeurIPS 2024
Irem Boybat, Manuel Le Gallo, et al.
Nature Communications