1.9k citations · 2.3k across the 15 of their papers we have counts for
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cs.AR2021★ 8 cited
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator
Chuteng Zhou, Fernando Garcia Redondo, Julian Büchel +7
Always-on TinyML perception tasks in IoT applications require very high energy efficiency. Analog compute-in-memory (CiM) using non-volatile memory (NVM) promises high efficiency a…
cs.LG2021★ 145 cited
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
Malte J. Rasch, Diego Moreda, Tayfun Gokmen +6
We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch…