898 citations · 3.4k across the 38 of their papers we have counts for
6 papers · 1 filter
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…
Accurate deep neural network inference using computational phase-change memory
Vinay Joshi, Manuel Le Gallo, Simon Haefeli +7
In-memory computing is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Crossbar arrays of resistive memory devices can be used to…
In-memory hyperdimensional computing
Geethan Karunaratne, Manuel Le Gallo, Giovanni Cherubini +3
Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed h…
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…
Monatomic phase change memory
Martin Salinga, Benedikt Kersting, Ider Ronneberger +7
Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way, with potential for neuromorphic computing application…
Low-Power Neuromorphic Hardware for Signal Processing Applications
Bipin Rajendran, Abu Sebastian, Michael Schmuker +2
Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting ma…