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Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST framework consists of enhancements by Elastic Mutation (EM) and Soft Memory Bound (&S) that ensure superior accuracy at high sparsity ratios. Different from the existing works for sparse training, this current work reveals the importance of sparsity schemes on the performance of sparse training in terms of accuracy as well as training speed on real edge devices. On top of that, the paper proposes to employ data efficiency for further acceleration of sparse training. Our results suggest that unforgettable examples can be identified in-situ even during the dynamic exploration of sparsity masks in the sparse training process, and therefore can be removed for further training speedup on edge devices. Comparing with state-of-the-art (SOTA) works on accuracy, our MEST increases Top-1 accuracy significantly on ImageNet when using the same unstructured sparsity scheme. Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works. A reviewer strongly against our work based on his false assumptions and misunderstandings. On top of the previous submission, we employ data efficiency for further acceleration of sparse training. And we explore the impact of model sparsity, sparsity schemes, and sparse training algorithms on the number of removable training examples. Our codes are publicly available at: https://github.com/boone891214/MEST.
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This content will become publicly available on May 17, 2026
RADIUS: RANGE-BASED GRADIENT SPARSITY FOR LARGE FOUNDATION MODEL PRE-TRAINING
We present Radius, a gradient sparsity algorithm and system to accelerate large foundation model (FM) training while preserving downstream task performance. Radius leverages two key insights in large FM pre-training: 1) only a small portion of gradients contribute to the model updates in each iteration, and 2) the spatial distribution of the gradients with large magnitude is stable over time. Radius overcomes the scaling problem of existing top-k sparsity methods, as it maintains the structure of sparse gradients thus avoids dense communication. We examine the convergence and speed of Radius on pre-training GPT models (355M and 2.0B) in data-parallel and compare it with the baseline top-k sparsification methods. Our results show that using the existing top-k method with AdamW optimizer fails to converge, and the training speed improvement with sparse communication is marginal. In contrast, Radius with 40% sparsity reduces per-step training time by 21% (19% for overall training time) across 64 NVIDIA A100 GPUs that are connected by the Slingshot 11 interconnect while preserving the downstream task performance.
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- Award ID(s):
- 2340011
- PAR ID:
- 10591330
- Publisher / Repository:
- Eighth Conference on Machine Learning and Systems
- Date Published:
- Format(s):
- Medium: X
- Location:
- Santa Clara, CA
- Sponsoring Org:
- National Science Foundation
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