activity
20192026
most citedViTA: A Vision Transformer Inference Accelerator for Edge Applications

64 citations · 107 across the 35 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.AR2024

Voltage-Controlled Magnetic Tunnel Junction based ADC-less Global Shutter Processing-in-Pixel for Extreme-Edge Intelligence

Md Abdullah-Al Kaiser, Gourav Datta, Jordan Athas +5

The vast amount of data generated by camera sensors has prompted the exploration of energy-efficient processing solutions for deploying computer vision tasks on edge devices. Among…

cs.CV2024

Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors

Md Abdullah-Al Kaiser, Sreetama Sarkar, Peter A. Beerel +2

Current video-based computer vision (CV) applications typically suffer from high energy consumption due to reading and processing all pixels in a frame, regardless of their signifi…

cs.CV2024

MaskVD: Region Masking for Efficient Video Object Detection

Sreetama Sarkar, Gourav Datta, Souvik Kundu +3

Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications, particularly for tasks that require state-of-the-art Vision Transformers (ViTs). S…

cs.AR2024

Toward High Performance, Programmable Extreme-Edge Intelligence for Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ

Md Abdullah-Al Kaiser, Gourav Datta, Peter A. Beerel +1

The desire to empower resource-limited edge devices with computer vision (CV) must overcome the high energy consumption of collecting and processing vast sensory data. To address t…

cs.NE2024★ 2 cited

LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units

Zeyu Liu, Gourav Datta, Anni Li +1

Transformer models have demonstrated high accuracy in numerous applications but have high complexity and lack sequential processing capability making them ill-suited for many strea…