64 citations · 147 across the 44 of their papers we have counts for
8 papers · 1 filter
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
Gourav Datta, Zeyu Liu, Md Abdullah-Al Kaiser +6
Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a p…
QuantPipe: Applying Adaptive Post-Training Quantization for Distributed Transformer Pipelines in Dynamic Edge Environments
Haonan Wang, Connor Imes, Souvik Kundu +3
Pipeline parallelism has achieved great success in deploying large-scale transformer models in cloud environments, but has received less attention in edge environments. Unlike in c…
Self-Attentive Pooling for Efficient Deep Learning
Fang Chen, Gourav Datta, Souvik Kundu +1
Efficient custom pooling techniques that can aggressively trim the dimensions of a feature map and thereby reduce inference compute and memory footprint for resource-constrained co…
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference
Soumyabrata Talukder, Souvik Kundu, Ratnesh Kumar
Most sensor calibrations rely on the linearity and steadiness of their response characteristics, but practical sensors are nonlinear, and their response drifts with time, restricti…
Federated Learning of Large Models at the Edge via Principal Sub-Model Training
Yue Niu, Saurav Prakash, Souvik Kundu +2
Federated Learning (FL) is emerging as a popular, promising decentralized learning framework that enables collaborative training among clients, with no need to share private data b…
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
Gourav Datta, Souvik Kundu, Zihan Yin +11
Today's high resolution, high frame rate cameras in autonomous vehicles generate a large volume of data that needs to be transferred and processed by a downstream processor or mach…
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness
Souvik Kundu, Sairam Sundaresan, Massoud Pedram +1
Existing models that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on convolution operations conditioned with feature-wise linea…
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
Gourav Datta, Souvik Kundu, Zihan Yin +5
The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such…