Francesco Corti

Francesco Corti, Balz Maag, Joachim Schauer et al.

Deep learning models deployed on edge devices frequently encounter resource variability, which arises from fluctuating energy levels, timing constraints, or prioritization of other critical tasks within the system. State-of-the-art machine learning pipelines generate resource-agnostic models that are not capable to adapt at runtime. In this work, we introduce Resource-Efficient Deep Subnetworks (REDS) to tackle model adaptation to variable resources. In contrast to the state-of-the-art, REDS leverages structured sparsity constructively by exploiting permutation invariance of neurons, which allows for hardware-specific optimizations. Specifically, REDS achieves computational efficiency by (1) skipping sequential computational blocks identified by a novel iterative knapsack optimizer, and (2) taking advantage of data cache by re-arranging the order of operations in REDS computational graph. REDS supports conventional deep networks frequently deployed on the edge and provides computational benefits even for small and simple networks. We evaluate REDS on eight benchmark architectures trained on the Visual Wake Words, Google Speech Commands, Fashion-MNIST, CIFAR-10 and ImageNet-1K datasets, and test on four off-the-shelf mobile and embedded hardware platforms. We provide a theoretical result and empirical evidence demonstrating REDS' outstanding performance in terms of submodels' test set accuracy, and demonstrate an adaptation time in response to dynamic resource constraints of under 40$μ$s, utilizing a fully-connected network on Arduino Nano 33 BLE.

Francesco Corti, Rahim Entezari, Sara Hooker et al.

We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high sparsity levels, contrastive learning results in a higher number of misclassified examples relative to models trained with traditional cross-entropy loss. To understand this pronounced difference, we use metrics such as the number of PIEs (Hooker et al., 2019), Q-Score (Kalibhat et al., 2022), and PD-Score (Baldock et al., 2021) to measure the impact of pruning on the learned representation quality. Our analysis suggests the schedule of the pruning method implementation matters. We find that the negative impact of sparsity on the quality of the learned representation is the highest when pruning is introduced early on in the training phase.

Georg Slamanig, Francesco Corti, Olga Saukh

Parameter-efficient fine-tuning (PEFT) methods reduce the computational costs of updating deep learning models by minimizing the number of additional parameters used to adapt a model to a down- stream task. While extensively researched in large language models (LLMs), their application to smaller models used on edge devices, such as convolutional neural networks, remains underexplored. This paper benchmarks and analyzes popular PEFT methods on convolutional architectures typically deployed in resource-constrained edge environments. We evaluate LoRA, DoRA, and GaLore for updating standard and depthwise convolutional architectures to handle distribution shifts and accommodate unseen classes. We utilize recently proposed PyTorch profilers to compare the updated model performance and computational costs of these PEFT methods with traditional fine-tuning approaches. With resource efficiency in mind, we investigate their update behavior across different rank dimensions. We find that the evaluated PEFT methods are only half as memory-efficient when applied to depthwise-separable convolution architectures, compared to their efficiency with LLMs. Conversely, when targeting convolu- tional architectures optimized for edge deployment, adapter-based PEFT methods can reduce floating point operations (FLOPs) during model updates by up to 95%. These insights offer valuable guidance for selecting PEFT methods based on hardware constraints, performance requirements, and application needs. Our code is online.