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[HTML][HTML] Neuromorphic artificial intelligence systems
Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical
neural networks, have a number of fundamental limitations in comparison with the …
neural networks, have a number of fundamental limitations in comparison with the …
Temporal-wise attention spiking neural networks for event streams classification
How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing
Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …
Sequence approximation using feedforward spiking neural network for spatiotemporal learning: Theory and optimization methods
A dynamical system of spiking neurons with only feedforward connections can classify
spatiotemporal patterns without recurrent connections. However, the theoretical construct of …
spatiotemporal patterns without recurrent connections. However, the theoretical construct of …
Deep reinforcement learning with significant multiplications inference
We propose a sparse computation method for optimizing the inference of neural networks in
reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this …
reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this …
Asynchronous event processing with local-shift graph convolutional network
Event cameras are bio-inspired sensors that produce sparse and asynchronous event
streams instead of frame-based images at a high-rate. Recent works utilizing graph …
streams instead of frame-based images at a high-rate. Recent works utilizing graph …
Neuronflow: A hybrid neuromorphic–dataflow processor architecture for AI workloads
We present a novel computing architecture which combines the event-based and compute-
in-network principles of neuromorphic computing with a traditional dataflow architecture. The …
in-network principles of neuromorphic computing with a traditional dataflow architecture. The …
Heterogeneous neuronal and synaptic dynamics for spike-efficient unsupervised learning: Theory and design principles
This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the
spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction …
spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction …
Modeling learnable electrical synapse for high precision spatio-temporal recognition
Bio-inspired recipes are being introduced to artificial neural networks for the efficient
processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is …
processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is …
A TTFS-based energy and utilization efficient neuromorphic CNN accelerator
Spiking neural networks (SNNs), which are a form of neuromorphic, brain-inspired AI, have
the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes …
the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes …