1 citations · 1 across the 4 of their papers we have counts for
4 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…
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…
Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity
Matteo Grimaldi, Darshan C. Ganji, Ivan Lazarevich +1
The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment. Exploiting sparsity in the network's fe…
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…