动态分配模型计算任务，边端协同降低能耗72%。
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
- 分两阶段优化：离线找最优配置，在线按请求动态调整
- 实测能耗比纯云端低72%，延迟满足90%用户阈值
- 适合资源受限的边缘设备部署，尤其关注能效的场景
在边缘设备上部署机器学习模型面临计算资源与能源限制的挑战。虽然分割计算可将大型神经网络分解并在边缘与云端协同处理，但确定最佳分割层及硬件配置仍极为复杂。这主要受制于巨大的配置空间、软硬件参数间的非线性依赖、异构硬件与能源特性以及动态工作负载。为此，我们提出DynaSplit，一个两阶段协同设计框架，动态调节软件（如分割层）和硬件（如加速器使用、CPU频率）参数。离线阶段采用元启发式方法求解多目标优化问题以发现最优设置；在线阶段通过调度算法为新推理请求匹配最适配置并实时系统配置。我们在真实测试平台上评估了DynaSplit，使用多个主流预训练神经网络。实验结果表明，相比纯云端计算，能耗降低最高达72%，同时满足约90%用户请求的延迟阈值，优于基线方法。
原文摘要 · Abstract (English)
The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task. This process is in fact hindered by the large configuration space, the non-linear dependencies between software and hardware parameters, the heterogeneous hardware and energy characteristics, and the dynamic workload conditions. To overcome this challenge, we propose DynaSplit, a two-phase framework that dynamically configures parameters across both software (i.e., split layer) and hardware (e.g., accelerator usage, CPU frequency). During the Offline Phase, we solve a multi-objective optimization problem with a meta-heuristic approach to discover optimal settings. During the Online Phase, a scheduling algorithm identifies the most suitable settings for an incoming inference request and configures the system accordingly. We evaluate DynaSplit using popular pre-trained NNs on a real-world testbed. Experimental results show a reduction in energy consumption up to 72% compared to cloud-only computation, while meeting ~90% of user request's latency threshold compared to baselines.
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