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 …
[HTML][HTML] Deep learning with spiking neurons: Opportunities and challenges
M Pfeiffer, T Pfeil - Frontiers in neuroscience, 2018 - frontiersin.org
Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …
sparse and asynchronous binary signals are communicated and processed in a massively …
All-optical spiking neurosynaptic networks with self-learning capabilities
Software implementations of brain-inspired computing underlie many important
computational tasks, from image processing to speech recognition, artificial intelligence and …
computational tasks, from image processing to speech recognition, artificial intelligence and …
Neural heterogeneity promotes robust learning
The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the
neural level plays a functional role remains unclear, and has been relatively little explored in …
neural level plays a functional role remains unclear, and has been relatively little explored in …
Neuro-inspired computing with emerging nonvolatile memorys
S Yu - Proceedings of the IEEE, 2018 - ieeexplore.ieee.org
This comprehensive review summarizes state of the art, challenges, and prospects of the
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
NeuroSim: A circuit-level macro model for benchmarking neuro-inspired architectures in online learning
Neuro-inspired architectures based on synaptic memory arrays have been proposed for on-
chip acceleration of weighted sum and weight update in machine/deep learning algorithms …
chip acceleration of weighted sum and weight update in machine/deep learning algorithms …
In-memory learning with analog resistive switching memory: A review and perspective
In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …
their hardware technologies for in-memory learning, as well as their challenges and …
A tandem learning rule for effective training and rapid inference of deep spiking neural networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired
computing model for neuromorphic computing (NC) architectures. However, due to the …
computing model for neuromorphic computing (NC) architectures. However, due to the …
Sparse spiking gradient descent
N Perez-Nieves, D Goodman - Advances in Neural …, 2021 - proceedings.neurips.cc
There is an increasing interest in emulating Spiking Neural Networks (SNNs) on
neuromorphic computing devices due to their low energy consumption. Recent advances …
neuromorphic computing devices due to their low energy consumption. Recent advances …