Direct training high-performance deep spiking neural networks: a review of theories and methods
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
TripleBrain: A compact neuromorphic hardware core with fast on-chip self-organizing and reinforcement spike-timing dependent plasticity
H Wang, Z He, T Wang, J He, X Zhou… - … Circuits and Systems, 2022 - ieeexplore.ieee.org
Human brain cortex acts as a rich inspiration source for constructing efficient artificial
cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired …
cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired …
A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm
Spiking neural networks (SNNs) are more energy-and resource-efficient than artificial neural
networks (ANNs). However, supervised SNN learning is a challenging task due to non …
networks (ANNs). However, supervised SNN learning is a challenging task due to non …
Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge applications
JI Okonkwo, MS Abdelfattah, P Mirtaheri… - Frontiers in …, 2024 - frontiersin.org
Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in
smart circuits and autonomous robots will play an important role in the next industrial leap in …
smart circuits and autonomous robots will play an important role in the next industrial leap in …
Exploiting memristors for neuromorphic reinforcement learning
C Shi, J Lu, Y Wang, P Li, M Tian - 2021 IEEE 3rd International …, 2021 - ieeexplore.ieee.org
Memristors have been proposed to build neural networks for their nanoscale size, low power
consumption and high density. They are particularly suited to act as synaptic weights …
consumption and high density. They are particularly suited to act as synaptic weights …
Large-Scale Bio-Inspired FPGA Models for Path Planning
The hippocampus provides significant inspiration for spatial navigation and memory in both
humans and animals. Constructing large-scale spiking neural network (SNN) models based …
humans and animals. Constructing large-scale spiking neural network (SNN) models based …
Modeling and Designing of an All-Digital Resonate-and-Fire Neuron Circuit
Integrate-and-fire (IAF) and leaky integrate-and-fire (LIF) models are the popular models for
spiking neurons and spiking neuron networks (SNN). They lack the dynamic properties of …
spiking neurons and spiking neuron networks (SNN). They lack the dynamic properties of …
A reduced spiking neural network architecture for energy efficient context-dependent reinforcement learning tasks
H Rasheed, P Mirtaheri… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Neuromorphic circuits and systems involving spiking neural networks (SNN) have resulted
in disruptive advances in performance/joule for relevant applications. A novel reinforcement …
in disruptive advances in performance/joule for relevant applications. A novel reinforcement …
Efficient classification method for hyperspectral images based on spiking neural network
The complexity of convolutional neural network architectures in hyperspectral image
classification tasks results in long training times and high energy consumption, which …
classification tasks results in long training times and high energy consumption, which …
A Fully-Parallel Reconfigurable Spiking Neural Network Accelerator with Structured Sparse Connections
In this work, we present a fully parallel reconfigurable spiking neural network (SNN)
accelerator for various applications of edge computing. In contrast to conventional fully …
accelerator for various applications of edge computing. In contrast to conventional fully …