898 citations · 1.5k across the 22 of their papers we have counts for
8 papers · 1 filter
LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles
Corey Lammie, Yuxuan Wang, Flavio Ponzina +7
When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learnin…
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
Dominique J. Kösters, Bryan A. Kortman, Irem Boybat +10
The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performanc…
Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors
Sergi Abadal, Robert Guirado, Hamidreza Taghvaee +18
The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly c…
Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing
Anastasios Petropoulos, Irem Boybat, Manuel Le Gallo +3
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory…
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…
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…
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…
Neuromorphic computing with multi-memristive synapses
Irem Boybat, Manuel Le Gallo, S. R. Nandakumar +7
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which…