Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads

• URL: http://arxiv.org/abs/2310.11096v1
• Date: Tue, 17 Oct 2023 09:25:17 GMT
• Title: Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads
• Authors: Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris, Nicholas D. Lane
• Abstract summary: Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices. We propose Dysta, a novel scheduler that utilizes both static sparsity patterns and dynamic sparsity information for the sparse multi-DNN scheduling. Our proposed approach outperforms the state-of-the-art methods with up to 10% decrease in latency constraint violation rate and nearly 4X reduction in average normalized turnaround time.
• Score: 65.47816359465155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices, such as mobile phones where multiple tasks serve a single user for daily activities, and data centers, where various requests are raised from millions of users, as seen with large language models. To reduce the costly computational and memory requirements of these workloads, various efficient sparsification approaches have been introduced, resulting in widespread sparsity across different types of DNN models. In this context, there is an emerging need for scheduling sparse multi-DNN workloads, a problem that is largely unexplored in previous literature. This paper systematically analyses the use-cases of multiple sparse DNNs and investigates the opportunities for optimizations. Based on these findings, we propose Dysta, a novel bi-level dynamic and static scheduler that utilizes both static sparsity patterns and dynamic sparsity information for the sparse multi-DNN scheduling. Both static and dynamic components of Dysta are jointly designed at the software and hardware levels, respectively, to improve and refine the scheduling approach. To facilitate future progress in the study of this class of workloads, we construct a public benchmark that contains sparse multi-DNN workloads across different deployment scenarios, spanning from mobile phones and AR/VR wearables to data centers. A comprehensive evaluation on the sparse multi-DNN benchmark demonstrates that our proposed approach outperforms the state-of-the-art methods with up to 10% decrease in latency constraint violation rate and nearly 4X reduction in average normalized turnaround time. Our artifacts and code are publicly available at: https://github.com/SamsungLabs/Sparse-Multi-DNN-Scheduling.

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