129 citations · 740 across the 61 of their papers we have counts for
4 papers · 1 filter
RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are e…
EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under unsupervised settings and low operational power/energy due to their bio-plausible computatio…
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
Vojtech Mrazek, Zdenek Vasicek, Lukas Sekanina +2
The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to reco…
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks
Alberto Marchisio, Muhammad Abdullah Hanif, Semeen Rehman +2
Activation functions influence behavior and performance of DNNs. Nonlinear activation functions, like Rectified Linear Units (ReLU), Exponential Linear Units (ELU) and Scaled Expon…