28 citations · 71 across the 16 of their papers we have counts for
17 papers
ActNAS : Generating Efficient YOLO Models using Activation NAS
Sudhakar Sah, Ravish Kumar, Darshan C. Ganji +1
Activation functions introduce non-linearity into Neural Networks, enabling them to learn complex patterns. Different activation functions vary in speed and accuracy, ranging from…
Token Pruning using a Lightweight Background Aware Vision Transformer
Sudhakar Sah, Ravish Kumar, Honnesh Rohmetra +1
High runtime memory and high latency puts significant constraint on Vision Transformer training and inference, especially on edge devices. Token pruning reduces the number of input…
MCUBench: A Benchmark of Tiny Object Detectors on MCUs
Sudhakar Sah, Darshan C. Ganji, Matteo Grimaldi +4
We introduce MCUBench, a benchmark featuring over 100 YOLO-based object detection models evaluated on the VOC dataset across seven different MCUs. This benchmark provides detailed…
QGen: On the Ability to Generalize in Quantization Aware Training
MohammadHossein AskariHemmat, Ahmadreza Jeddi, Reyhane Askari Hemmat +6
Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the gener…
DeepliteRT: Computer Vision at the Edge
Saad Ashfaq, Alexander Hoffman, Saptarshi Mitra +3
The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications. However, these complex models require…
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables
Darshan C. Ganji, Saad Ashfaq, Ehsan Saboori +6
A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices. Quanti…