64 citations · 256 across the 76 of their papers we have counts for
11 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…
Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Gourav Datta, Zeyu Liu, Peter A. Beerel
Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing paradigm for a wide range of low-power vision tasks. However, state-of-the-art (SOTA) SNN mode…
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…
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…
Enabling ISP-less Low-Power Computer Vision
Gourav Datta, Zeyu Liu, Zihan Yin +3
In order to deploy current computer vision (CV) models on resource-constrained low-power devices, recent works have proposed in-sensor and in-pixel computing approaches that try to…
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…