Spiking neural network integrated circuits: A review of trends and future directions
The rapid growth of deep learning, spurred by its successes in various fields ranging from
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
An optical neural network using less than 1 photon per multiplication
Deep learning has become a widespread tool in both science and industry. However,
continued progress is hampered by the rapid growth in energy costs of ever-larger deep …
continued progress is hampered by the rapid growth in energy costs of ever-larger deep …
SNIB: improving spike-based machine learning using nonlinear information bottleneck
S Yang, B Chen - IEEE Transactions on Systems, Man, and …, 2023 - ieeexplore.ieee.org
Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …
general intelligence (AGI) research due to their low power consumption, high computational …
Spike-driven multi-scale learning with hybrid mechanisms of spiking dendrites
Neural dendrites play a critical role in various cognitive functions, including spatial
navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal …
navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal …
Training an ising machine with equilibrium propagation
Ising machines, which are hardware implementations of the Ising model of coupled spins,
have been influential in the development of unsupervised learning algorithms at the origins …
have been influential in the development of unsupervised learning algorithms at the origins …
Learning where to learn: Gradient sparsity in meta and continual learning
Finding neural network weights that generalize well from small datasets is difficult. A
promising approach is to learn a weight initialization such that a small number of weight …
promising approach is to learn a weight initialization such that a small number of weight …
Bottom-up and top-down approaches for the design of neuromorphic processing systems: tradeoffs and synergies between natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …
Nadol: Neuromorphic architecture for spike-driven online learning by dendrites
S Yang, H Wang, Y Pang, MR Azghadi… - … Circuits and Systems, 2023 - ieeexplore.ieee.org
Biologically plausible learning with neuronal dendrites is a promising perspective to improve
the spike-driven learning capability by introducing dendritic processing as an additional …
the spike-driven learning capability by introducing dendritic processing as an additional …
Bottom-up and top-down neural processing systems design: Neuromorphic intelligence as the convergence of natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
While the backpropagation of error algorithm enables deep neural network training, it
implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and …
implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and …