Physics for neuromorphic computing
Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …
for information processing, capable of highly sophisticated tasks. Systems built with standard …
[HTML][HTML] Reliability of analog resistive switching memory for neuromorphic computing
As artificial intelligence calls for novel energy-efficient hardware, neuromorphic computing
systems based on analog resistive switching memory (RSM) devices have drawn great …
systems based on analog resistive switching memory (RSM) devices have drawn great …
Equivalent-accuracy accelerated neural-network training using analogue memory
Neural-network training can be slow and energy intensive, owing to the need to transfer the
weight data for the network between conventional digital memory chips and processor chips …
weight data for the network between conventional digital memory chips and processor chips …
Emerging neuromorphic devices
D Ielmini, S Ambrogio - Nanotechnology, 2019 - iopscience.iop.org
Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical
way, by enabling machine learning in the industry, business, health, transportation, and …
way, by enabling machine learning in the industry, business, health, transportation, and …
[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 …
[HTML][HTML] Tutorial: Brain-inspired computing using phase-change memory devices
There is a significant need to build efficient non-von Neumann computing systems for highly
data-centric artificial intelligence related applications. Brain-inspired computing is one such …
data-centric artificial intelligence related applications. Brain-inspired computing is one such …
Holomorphic equilibrium propagation computes exact gradients through finite size oscillations
A Laborieux, F Zenke - Advances in neural information …, 2022 - proceedings.neurips.cc
Equilibrium propagation (EP) is an alternative to backpropagation (BP) that allows the
training of deep neural networks with local learning rules. It thus provides a compelling …
training of deep neural networks with local learning rules. It thus provides a compelling …
Recent progress in analog memory-based accelerators for deep learning
We survey recent progress in the use of analog memory devices to build neuromorphic
hardware accelerators for deep learning applications. After an overview of deep learning …
hardware accelerators for deep learning applications. After an overview of deep learning …
Learning in memristive neural network architectures using analog backpropagation circuits
O Krestinskaya, KN Salama… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The on-chip implementation of learning algorithms would speed up the training of neural
networks in crossbar arrays. The circuit level design and implementation of a back …
networks in crossbar arrays. The circuit level design and implementation of a back …
Resistive crossbars as approximate hardware building blocks for machine learning: Opportunities and challenges
Traditional computing systems based on the von Neumann architecture are fundamentally
bottlenecked by data transfers between processors and memory. The emergence of data …
bottlenecked by data transfers between processors and memory. The emergence of data …