53 citations · 86 across the 10 of their papers we have counts for
11 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…
Traces Propagation: Memory-Efficient and Scalable Forward-Only Learning in Spiking Neural Networks
Lorenzo Pes, Bojian Yin, Sander Stuijk +1
Spiking Neural Networks (SNNs) provide an efficient framework for processing dynamic spatio-temporal signals and for investigating the learning principles underlying biological neu…
STEMS: Spatial-Temporal Mapping For Spiking Neural Networks
Sherif Eissa, Sander Stuijk, Floran De Putter +3
Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural…
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…
Probabilistic Inference in the Era of Tensor Networks and Differential Programming
Martin Roa-Villescas, Xuanzhao Gao, Sander Stuijk +2
Probabilistic inference is a fundamental task in modern machine learning. Recent advances in tensor network (TN) contraction algorithms have enabled the development of better exact…
Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors
Wei Sun, Ang Li, Tong Geng +2
Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users ha…
Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
Gagandeep Singh, Rakesh Nadig, Jisung Park +7
Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques t…
Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric
Gagandeep Singh, Dionysios Diamantopoulos, Juan Gómez-Luna +4
Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU impl…
NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling
Gagandeep Singh, Dionysios Diamantopoulos, Christoph Hagleitner +4
Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU impl…
Near-Memory Computing: Past, Present, and Future
Gagandeep Singh, Lorenzo Chelini, Stefano Corda +5
The conventional approach of moving data to the CPU for computation has become a significant performance bottleneck for emerging scale-out data-intensive applications due to their…