12 citations · 82 across the 51 of their papers we have counts for
6 papers · 1 filter
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Zeyu Liu, Gourav Datta, Anni Li +1
Transformer models have demonstrated high accuracy in numerous applications but have high complexity and lack sequential processing capability making them ill-suited for many strea…
Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMs
Gourav Datta, Haoqin Deng, Robert Aviles +1
Spiking Neural Networks (SNNs) have emerged as an attractive spatio-temporal computing paradigm for complex vision tasks. However, most existing works yield models that require man…
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…
Accelerating Training of Deep Neural Networks via Sparse Edge Processing
Sourya Dey, Yinan Shao, Keith M. Chugg +1
We propose a reconfigurable hardware architecture for deep neural networks (DNNs) capable of online training and inference, which uses algorithmically pre-determined, structured sp…