[HTML][HTML] Prospects and challenges of electrochemical random-access memory for deep-learning accelerators
J Cui, H Liu, Q Cao - Current Opinion in Solid State and Materials Science, 2024 - Elsevier
The ever-expanding capabilities of machine learning are powered by exponentially growing
complexity of deep neural network (DNN) models, requiring more energy and chip-area …
complexity of deep neural network (DNN) models, requiring more energy and chip-area …
Device Specifications for Neural Network Training with Analog Resistive Cross‐Point Arrays Using Tiki‐Taka Algorithms
Recently, specialized training algorithms for analog cross‐point array‐based neural network
accelerators have been introduced to counteract device non‐idealities such as update …
accelerators have been introduced to counteract device non‐idealities such as update …