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STResNet & STYOLO : A New Family of Compact Classification and Object Detection Models for MCUs
Sudhakar Sah, Ravish Kumar
Recent advancements in lightweight neural networks have significantly improved the efficiency of deploying deep learning models on edge hardware. However, most existing architectur…
CompressNAS : A Fast and Efficient Technique for Model Compression using Decomposition
Sudhakar Sah, Nikhil Chabbra, Matthieu Durnerin
Deep Convolutional Neural Networks (CNNs) are increasingly difficult to deploy on microcontrollers (MCUs) and lightweight NPUs (Neural Processing Units) due to their growing size a…
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…
YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems
Ivan Lazarevich, Matteo Grimaldi, Ravish Kumar +3
We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia…