3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
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…
QReg: On Regularization Effects of Quantization
MohammadHossein AskariHemmat, Reyhane Askari Hemmat, Alex Hoffman +6
In this paper we study the effects of quantization in DNN training. We hypothesize that weight quantization is a form of regularization and the amount of regularization is correlat…