64 citations · 256 across the 76 of their papers we have counts for
16 papers · 1 filter
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
Gourav Datta, Zeyu Liu, James Diffenderfer +2
Bio-inspired Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to intricate convolutional neural networks (CNN), all while delivering remarkable energy and la…
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology
Souvik Kundu, Rui-Jie Zhu, Akhilesh Jaiswal +1
Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing appli…
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
Gourav Datta, Zeyu Liu, Anni Li +1
Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly r…
FixPix: Fixing Bad Pixels using Deep Learning
Sreetama Sarkar, Xinan Ye, Gourav Datta +1
Efficient and effective on-line detection and correction of bad-pixels can improve yield and increase the expected lifetime of image sensors. This paper presents a comprehensive De…
Let's Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Types
Yue Hu, Gourav Datta, Kira Beerel +1
Computer vision (CV) pipelines are typically evaluated on datasets processed by image signal processing (ISP) pipelines even though, for resource-constrained applications, an impor…
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
Chenghao Li, Dake Chen, Yuke Zhang +1
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent…