11 citations · 27 across the 3 of their papers we have counts for
4 papers
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
Martino Dazzi, Abu Sebastian, Pier Andrea Francese +3
In-memory computing is an emerging computing paradigm that could enable deeplearning inference at significantly higher energy efficiency and reduced latency. The essential idea is…
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
S. R. Nandakumar, Irem Boybat, Manuel Le Gallo +3
Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The…
In-memory computing on a photonic platform
Carlos Ríos, Nathan Youngblood, Zengguang Cheng +5
Collocated data processing and storage are the norm in biological systems. Indeed, the von Neumann computing architecture, that physically and temporally separates processing and m…
Mixed-precision training of deep neural networks using computational memory
Nandakumar S. R., Manuel Le Gallo, Irem Boybat +3
Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speec…