Exact Memory- and Communication-aware Scheduling of DNNs on Pipelined Edge TPUs

Abstract

Deep neural networks (DNNs) represent the state-of-the-art in many applications but have substantial computational and memory requirements, which greatly limit their training and deployment in real-world systems. In particular, the deployment challenges further increase on edge systems with much more restricted resource-constrained (e.g., computation and memory bounded), which recently attracted significant interest in many application scenarios. Such devices like Edge TPUs usually provide limited on-chip storage and memory bandwidth, where the heuristic-based ahead-of-time compilation techniques are highly limited in optimizing the inference performance due to the lacks of performance guarantees. This work proposes a novel exact pipeline scheduling framework that enables model parameter caching, data dependency, and device-to-device communication-aware multi-objective optimizations. The framework is powered by novel versatile SDC+ILP formulations supporting both propositional logic and non-equality constraints. The experimental results demonstrate that the proposed scheduling frameworks consistently outperform commercial Edge TPU Compiler with up to more than 4 x speedups on eleven ImageNet models in physical pipelined Edge TPU setups. In addition, we have demonstrated consistent real-world energy efficiency improvements measured with high precision power meter. Finally, the proposed framework has also demonstrated the capability in multi-model co-deployment on pipeline Edge TPU system, which is not supported by Edge TPU Compiler.