no code implementations • 17 Apr 2024 • MohammadHossein AskariHemmat, Ahmadreza Jeddi, Reyhane Askari Hemmat, Ivan Lazarevich, Alexander Hoffman, Sudhakar Sah, Ehsan Saboori, Yvon Savaria, Jean-Pierre David
In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance.
1 code implementation • 12 Sep 2023 • Matteo Grimaldi, Darshan C. Ganji, Ivan Lazarevich, Sudhakar Sah
The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment.
1 code implementation • 26 Jul 2023 • Ivan Lazarevich, Matteo Grimaldi, Ravish Kumar, Saptarshi Mitra, Shahrukh Khan, Sudhakar Sah
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 GPU, NPU).
no code implementations • 24 Jun 2022 • MohammadHossein AskariHemmat, Reyhane Askari Hemmat, Alex Hoffman, Ivan Lazarevich, Ehsan Saboori, Olivier Mastropietro, Yvon Savaria, Jean-Pierre David
To confirm our analytical study, we performed an extensive list of experiments summarized in this paper in which we show that the regularization effects of quantization can be seen in various vision tasks and models, over various datasets.
no code implementations • 30 Apr 2021 • Ivan Lazarevich, Alexander Kozlov, Nikita Malinin
We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices.
2 code implementations • 20 Feb 2020 • Alexander Kozlov, Ivan Lazarevich, Vasily Shamporov, Nikolay Lyalyushkin, Yury Gorbachev
In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).
Ranked #3 on Binarization on ImageNet
3 code implementations • 9 Oct 2018 • Ivan Lazarevich, Ilya Prokin, Boris Gutkin, Victor Kazantsev
Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks.