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Souvik Kundu

Intel Labs

48 papers hereh-index 201.4k citations65 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author15
  • middle author32
  • last author1

Across the 48 of 48 papers where every author was matched, so the position is known.

fields
  • cs.CV19
  • cs.LG8
  • cs.CL5
  • cs.AR3
  • cs.DC3
  • cs.NE3
affiliations
  • Intel Labs
same name
  • Souvik Kundu — 16 papers, h 8
  • Souvik Kundu — 7 papers, h 4
  • Souvik Kundu — 7 papers, h 3
  • Souvik Kundu — 6 papers, h 6
  • Souvik Kundu — 5 papers, h 9
  • Souvik Kundu — 5 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedViTA: A Vision Transformer Inference Accelerator for Edge Applications

64 citations · 133 across the 42 of their papers we have counts for

collaborators
Showing cs.NEShow all

3 papers · 1 filter

cs.NE2021★ 5 cited

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…

cs.NE2021★ 1 cited

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…

cs.NE2021★ 12 cited

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…

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