Brain-inspired computing needs a master plan
New computing technologies inspired by the brain promise fundamentally different ways to
process information with extreme energy efficiency and the ability to handle the avalanche of …
process information with extreme energy efficiency and the ability to handle the avalanche of …
Prospects and applications of photonic neural networks
C Huang, VJ Sorger, M Miscuglio… - … in Physics: X, 2022 - Taylor & Francis
Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …
learning, and neuromorphic computing. Software implementations of neural networks on …
[HTML][HTML] An analog-AI chip for energy-efficient speech recognition and transcription
Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks
To tackle important combinatorial optimization problems, a variety of annealing-inspired
computing accelerators, based on several different technology platforms, have been …
computing accelerators, based on several different technology platforms, have been …
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional
computing through parallel programming and readout of artificial neural network weights in …
computing through parallel programming and readout of artificial neural network weights in …
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Memristors with tunable resistance states are emerging building blocks of artificial neural
networks. However, in situ learning on a large-scale multiple-layer memristor network has …
networks. However, in situ learning on a large-scale multiple-layer memristor network has …
Probabilistic neural computing with stochastic devices
S Misra, LC Bland, SG Cardwell… - Advanced …, 2023 - Wiley Online Library
The brain has effectively proven a powerful inspiration for the development of computing
architectures in which processing is tightly integrated with memory, communication is event …
architectures in which processing is tightly integrated with memory, communication is event …
[HTML][HTML] Analog architectures for neural network acceleration based on non-volatile memory
Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …
have the potential to overcome the major bottlenecks faced by digital hardware for data …
In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and …
The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …
von Neumann machines does not support the requirements of emerging applications that …
ECRAM materials, devices, circuits and architectures: A perspective
Non‐von‐Neumann computing using neuromorphic systems based on two‐terminal
resistive nonvolatile memory elements has emerged as a promising approach, but its full …
resistive nonvolatile memory elements has emerged as a promising approach, but its full …