64 citations · 107 across the 35 of their papers we have counts for
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Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?
Gourav Datta, Peter A. Beerel
Spiking neural networks (SNNs), that operate via binary spikes distributed over time, have emerged as a promising energy efficient ML paradigm for resource-constrained devices. How…
A High Performance and Robust FIFO Synchronizer-Interface for Crossing Clock Domains in SFQ Logic
Gourav Datta, Shidie Lin, Peter A. Beerel
Digital single-flux quantum (SFQ) technology promises to meet the demands of ultra low power and high speed computing needed for future exascale supercomputing platforms. However,…
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification
Gourav Datta, Souvik Kundu, Akhilesh R. Jaiswal +1
Hyper spectral images (HSI) provide rich spectral and spatial information across a series of contiguous spectral bands. However, the accurate processing of the spectral and spatial…
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding
Gourav Datta, Souvik Kundu, Peter A. Beerel
Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional deep learning frameworks, since they provide higher computational efficiency in event driven…
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression
Souvik Kundu, Gourav Datta, Massoud Pedram +1
Deep spiking neural networks (SNNs) have emerged as a potential alternative to traditional deep learning frameworks, due to their promise to provide increased compute efficiency on…