480 citations · 1.2k across the 8 of their papers we have counts for
5 papers · 1 filter
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Manuel Le Gallo, Corey Lammie, Julian Buechel +8
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and n…
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…
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…
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 In-Memory Computing
Manuel Le Gallo, Abu Sebastian, Roland Mathis +6
As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order t…