Edge AI systems often operate under stringent energy and volume constraints that demand extreme efficiency under limited battery capacity, with requirements worsening as intelligent capability demands advance. Prior literature suggests that fine-grained power orchestration, including DVFS and power gating, enables significant energy efficiency benefits that cannot be left unexploited, while still exhibiting unexplored challenges. We observe that layer-level approaches incur unintended overheads due to inter-layer coupling of power control decisions, and that jointly managing these mechanisms under practical constraints such as limited voltage rails and transition overheads leads to a rapidly growing combinatorial schedule space. To address this, we propose PowerFlow-DNN, a compiler-directed framework for end-to-end power-state orchestration in ultra-low-power accelerators. By constructing a rigorous problem formulation for deadline-constrained, real-time, periodic inference as a unified inter-layer power-scheduling problem, our framework enables automated discovery of energy-minimal power-state schedules that adhere to a deadline while accounting for end-to-end, inter-layer impacts. We evaluate the framework on a DNN accelerator VLSI implementation in TSMC 40nm technology. Across representative edge networks, we show that PowerFlow-DNN discovers near-optimal solutions under the discretized formulation and achieves energy within 0.68\% of the exact ILP oracle, reducing energy by up to 37\% compared to an aggressive baseline without power orchestration, while reasoning over a combinatorial schedule space of over $10^{160}$ possible power-state assignments, yet operating on a structured layered state graph that enables efficient optimization, achieving up to 2.14$\times$ solver speedup via lightweight pruning.

翻译：边缘AI系统通常在严格的能量与体积约束下运行，要求在有限的电池容量下实现极致能效，且随着智能能力需求的提升，这些要求愈发严苛。已有研究表明，包括DVFS和电源门控在内的细粒度功耗编排能够带来不可忽视的显著能效提升，但仍存在待探索的挑战。我们发现，基于层级的方案因功率控制决策的层间耦合而引入非预期开销，且在有限电压轨和状态转换开销等实际约束下协同管理这些机制，会导致组合调度空间呈指数级增长。为此，我们提出PowerFlow-DNN——一种面向超低功耗加速器端到端功耗状态编排的编译器制导框架。通过将受截止时间约束的实时周期性推理过程构建为统一的层间功率调度问题的严格形式化模型，我们的框架能够自动发现满足截止时间要求且考虑端到端层间影响的能量最小化功率状态调度方案。我们在基于台积电40纳米工艺实现的DNN加速器VLSI上对该框架进行了评估。针对代表性边缘网络的实验表明，PowerFlow-DNN能够在离散化公式化方法下发现近最优解，其能耗与精确ILP基准方案相比差异在0.68%以内，相比未进行功耗编排的激进基线方案节能高达37%；同时，该框架可对超过10^160种可能功率状态赋值的组合调度空间进行推理，并通过结构化分层状态图实现高效优化，借助轻量级剪枝策略获得最高2.14倍的求解器加速比。