294 citations · 379 across the 16 of their papers we have counts for
7 papers · 1 filter
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic +26
The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of…
In-memory factorization of holographic perceptual representations
Jovin Langenegger, Geethan Karunaratne, Michael Hersche +3
Disentanglement of constituent factors of a sensory signal is central to perception and cognition and hence is a critical task for future artificial intelligence systems. In this p…
Energy Efficient In-memory Hyperdimensional Encoding for Spatio-temporal Signal Processing
Geethan Karunaratne, Manuel Le Gallo, Michael Hersche +4
The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks com…
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…
Robust High-dimensional Memory-augmented Neural Networks
Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo +4
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapti…
Mixed-precision deep learning based on computational memory
S. R. Nandakumar, Manuel Le Gallo, Christophe Piveteau +11
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition…