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cs.LG2025
Analog Foundation Models
Julian Büchel, Iason Chalas, Giovanni Acampa +7
Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-ba…
cs.LG2020
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…