64 citations · 147 across the 44 of their papers we have counts for
6 papers · 1 filter
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch
Souvik Kundu, Shikai Wang, Qirui Sun +2
Large DNNs with mixed-precision quantization can achieve ultra-high compression while retaining high classification performance. However, because of the challenges in finding an ac…
Pipeline Parallelism for Inference on Heterogeneous Edge Computing
Yang Hu, Connor Imes, Xuanang Zhao +4
Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision (CV) and natural language processing (NLP). However, these large-scale mod…
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise
Souvik Kundu, Massoud Pedram, Peter A. Beerel
Low-latency deep spiking neural networks (SNNs) have become a promising alternative to conventional artificial neural networks (ANNs) because of their potential for increased energ…
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…