Samuel Tardieu

Maxime Girard, Victor Quétu, Samuel Tardieu et al.

Deploying Deep Neural Networks (DNNs) on different hardware platforms is challenging due to varying resource constraints. Besides handcrafted approaches aiming at making deep models hardware-friendly, Neural Architectures Search is rising as a toolbox to craft more efficient DNNs without sacrificing performance. Among these, the Once-For-All (OFA) approach offers a solution by allowing the sampling of well-performing sub-networks from a single supernet -- this leads to evident advantages in terms of computation. However, OFA does not fully utilize the potential memory capacity of the target device, focusing instead on limiting maximum memory usage per layer. This leaves room for an unexploited potential in terms of model generalizability. In this paper, we introduce a Memory-Optimized OFA (MOOFA) supernet, designed to enhance DNN deployment on resource-limited devices by maximizing memory usage (and for instance, features diversity) across different configurations. Tested on ImageNet, our MOOFA supernet demonstrates improvements in memory exploitation and model accuracy compared to the original OFA supernet. Our code is available at https://github.com/MaximeGirard/memory-optimized-once-for-all.

Solal Rapaport, Laurent Pautet, Samuel Tardieu et al.

Git tags are commonly viewed as immutable references in software development, marking releases and specific repository states that underpin build reproducibility and software supply-chain integrity. Despite their intended immutability, Git allows tags to be altered through deletion or modification via force-pushed updates. The prevalence of such alterations threatens reproducible builds and dependency integrity. We conduct the first large-scale empirical study of tag alterations in public code repositories, analyzing 328.4 M software repositories from Software Heritage and identifying 10.2 M tag alterations affecting 189 k unique repositories. A cross-analysis with Nixpkgs reveals that 32 packages reference tags altered in our dataset, with 7 exhibiting confirmed build errors, providing concrete evidence that tag alterations break reproducible package builds. Our findings challenge the widespread assumption that tags are immutable anchors for released software. We therefore recommend that build systems and package managers pin dependencies to cryptographic commit hashes, that development forges expose tagmutation audit logs, and that the community adopt systematic monitoring of tag alterations as a standard supply-chain security practice.

Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione et al.

Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI. While on-device inference is a well-explored topic in recent research, backpropagation remains an open challenge due to its prohibitive computational and memory costs compared to the extreme resource constraints of embedded devices. Drawing on tensor decomposition research, we tackle the main bottleneck of backpropagation, namely the memory footprint of activation map storage. We investigate and compare the effects of activation compression using Singular Value Decomposition and its tensor variant, High-Order Singular Value Decomposition. The application of low-order decomposition results in considerable memory savings while preserving the features essential for learning, and also offers theoretical guarantees to convergence. Experimental results obtained on main-stream architectures and tasks demonstrate Pareto-superiority over other state-of-the-art solutions, in terms of the trade-off between generalization and memory footprint.