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[HTML][HTML] Deep learning with spiking neurons: Opportunities and challenges
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 …
Deep learning in spiking neural networks
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event …
because the neurons in the networks are sparsely activated and computations are event …
Optimal ANN-SNN conversion for fast and accurate inference in deep spiking neural networks
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have
attracted great attentions from researchers and industry. The most efficient way to train deep …
attracted great attentions from researchers and industry. The most efficient way to train deep …
A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS
Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
Deep learning in microscopy image analysis: A survey
Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
Backpropagation for energy-efficient neuromorphic computing
Solving real world problems with embedded neural networks requires both training
algorithms that achieve high performance and compatible hardware that runs in real time …
algorithms that achieve high performance and compatible hardware that runs in real time …
Minerva: Enabling low-power, highly-accurate deep neural network accelerators
The continued success of Deep Neural Networks (DNNs) in classification tasks has sparked
a trend of accelerating their execution with specialized hardware. While published designs …
a trend of accelerating their execution with specialized hardware. While published designs …
Brain-inspired computing: A systematic survey and future trends
Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …
theories, models, hardware architectures, and application systems toward more general …
A survey on neuromorphic computing: Models and hardware
The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …
on traditional computer systems. As the performance of traditional Von Neumann machines …