Spiking neural networks and their applications: A review
The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …
domains. However, deep neural networks are very resource-intensive in terms of energy …
Computing of neuromorphic materials: an emerging approach for bioengineering solutions
The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …
Q-spinn: A framework for quantizing spiking neural networks
A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs)
without decreasing the accuracy significantly is quantization. However, the state-of-the-art …
without decreasing the accuracy significantly is quantization. However, the state-of-the-art …
Fspinn: An optimization framework for memory-efficient and energy-efficient spiking neural networks
Spiking neural networks (SNNs) are gaining interest due to their event-driven processing
which potentially consumes low-power/energy computations in hardware platforms while …
which potentially consumes low-power/energy computations in hardware platforms while …
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 …
lpspikecon: Enabling low-precision spiking neural network processing for efficient unsupervised continual learning on autonomous agents
Recent advances have shown that Spiking Neural Network (SNN)-based systems can
efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule …
efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule …
On the self-repair role of astrocytes in STDP enabled unsupervised SNNs
Neuromorphic computing is emerging to be a disruptive computational paradigm that
attempts to emulate various facets of the underlying structure and functionalities of the brain …
attempts to emulate various facets of the underlying structure and functionalities of the brain …
RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults
To maximize the performance and energy efficiency of Spiking Neural Network (SNN)
processing on resource-constrained embedded systems, specialized hardware …
processing on resource-constrained embedded systems, specialized hardware …
Mantis: enabling energy-efficient autonomous mobile agents with spiking neural networks
Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile robots
have shown huge potential for improving human productivity. These mobile agents require …
have shown huge potential for improving human productivity. These mobile agents require …
tinySNN: Towards memory-and energy-efficient spiking neural networks
Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher
accuracy. However, employing such models on the resource-and energy-constrained …
accuracy. However, employing such models on the resource-and energy-constrained …