5 papers
GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection
Hetian Liu, Jin Cui, Mengcheng Shi +4
On-device training of deep neural networks is fundamentally constrained by the computational and memory costs of large-scale datasets. Coreset selection offers a practical solution…
CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection
Boran Zhao, Hetian Liu, Zhenxian Hu +3
The training of large multimodal models fundamentally relies on massive image-text datasets, which inevitably incur prohibitive computational overhead. Dataset selection offers a p…
IMMSched: Interruptible Multi-DNN Scheduling via Parallel Multi-Particle Optimizing Subgraph Isomorphism
Boran Zhao, Hetian Liu, Zihang Yuan +4
The growing demand for multi-DNN workloads with unpredictable task arrival times has highlighted the need for interruptible scheduling on edge accelerators. However, existing preem…
AdapSNE: Adaptive Fireworks-Optimized and Entropy-Guided Dataset Sampling for Edge DNN Training
Boran Zhao, Hetian Liu, Zihang Yuan +5
Training deep neural networks (DNNs) directly on edge devices has attracted increasing attention, as it offers promising solutions to challenges such as domain adaptation and priva…
SparseMap: A Sparse Tensor Accelerator Framework Based on Evolution Strategy
Boran Zhao, Haiming Zhai, Zihang Yuan +4
The growing demand for sparse tensor algebra (SpTA) in machine learning and big data has driven the development of various sparse tensor accelerators. However, most existing manual…