4 papers
Mega: A 22 nm Convolutional Spiking Neural Network Accelerator Achieving 0.375 pJ/SOP for Efficient Edge Vision
Rick Luiken, Manil Dev Gomony, Sander Stuijk
Convolutional Spiking Neural Networks (SNN) offer the potential for highly energy-efficient vision processing by exploiting sparse, event-driven computation. However, existing SNN…
LOKI: a 0.266 pJ/SOP Digital SNN Accelerator with Multi-Cycle Clock-Gated SRAM in 22nm
Rick Luiken, Lorenzo Pes, Manil Dev Gomony +1
Bio-inspired sensors like Dynamic Vision Sensors (DVS) and silicon cochleas are often combined with Spiking Neural Networks (SNNs), enabling efficient, event-driven processing simi…
Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs
Lorenzo Pes, Maryam Dehbashizadeh Chehreghan, Rick Luiken +3
This work evaluates a forward-only learning algorithm on the MNIST dataset with hardware-in-the-loop training of a 4f optical correlator, achieving 87.6% accuracy with O(n2) comple…
Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks
Lorenzo Pes, Rick Luiken, Federico Corradi +1
While the human brain efficiently adapts to new tasks from a continuous stream of information, neural network models struggle to learn from sequential information without catastrop…