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04/08/2023
RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
To maximize the performance and energy efficiency of Spiking Neural Netw...
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04/08/2023
EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high...
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03/03/2023
TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents
Autonomous mobile agents require low-power/energy-efficient machine lear...
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12/24/2022
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks
Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mob...
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06/17/2022
tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks
Larger Spiking Neural Network (SNN) models are typically favorable as th...
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05/24/2022
lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents
Recent advances have shown that SNN-based systems can efficiently perfor...
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03/10/2022
SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors
Specialized hardware accelerators have been designed and employed to max...
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09/20/2021
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework
The security and privacy concerns along with the amount of data that is ...
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08/23/2021
ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories
Spiking neural networks (SNNs) have shown a potential for having low ene...
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07/05/2021
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
A prominent technique for reducing the memory footprint of Spiking Neura...
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02/28/2021
SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments
Spiking Neural Networks (SNNs) bear the potential of efficient unsupervi...
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02/28/2021
SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM
Spiking Neural Networks (SNNs) have the potential for achieving low ener...
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07/17/2020
FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) are gaining interest due to their event-d...
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04/21/2020
DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks
Many convolutional neural network (CNN) accelerators face performance- a...
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02/04/2019
ROMANet: Fine-Grained Reuse-Driven Data Organization and Off-Chip Memory Access Management for Deep Neural Network Accelerators
Many hardware accelerators have been proposed to improve the computation...
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10/30/2018
