898 citations · 2.7k across the 20 of their papers we have counts for
6 papers · 1 filter
Optimality of short-term synaptic plasticity in modelling certain dynamic environments
Timoleon Moraitis, Abu Sebastian, Evangelos Eleftheriou
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication a…
Online Spatio-Temporal Learning in Deep Neural Networks
Thomas Bohnstingl, Stanisław Woźniak, Wolfgang Maass +2
Biological neural networks are equipped with an inherent capability to continuously adapt through online learning. This aspect remains in stark contrast to learning with error back…
Compiling Neural Networks for a Computational Memory Accelerator
Kornilios Kourtis, Martino Dazzi, Nikolas Ioannou +3
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computation…
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…
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…
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…