1 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
Dynamic ConvNets on Tiny Devices via Nested Sparsity
Matteo Grimaldi, Luca Mocerino, Antonio Cipolletta +1
This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks…
EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNets
Matteo Grimaldi, Valentino Peluso, Andrea Calimera
The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression strategies capable of shrin…