Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
This work addresses performance and resource management for on-body AI applications on wearables, representing an incremental advance in system optimization for edge computing.
The paper tackles the challenge of running multiple AI apps efficiently on wearable devices with tiny AI accelerators by introducing Synergy, a system that coordinates accelerator use and parallelization, resulting in a 23.0x throughput improvement, 73.9% latency reduction, and 15.8% power savings compared to baselines.
The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrated into wearable devices, these accelerators open exciting opportunities, allowing various AI apps to run directly on the body. We present Synergy that provides AI apps with best-effort performance via system-driven holistic collaboration over AI accelerator-equipped wearables. To achieve this, Synergy provides device-agnostic programming interfaces to AI apps, giving the system visibility and controllability over the app's resource use. Then, Synergy maximizes the inference throughput of concurrent AI models by creating various execution plans for each app considering AI accelerator availability and intelligently selecting the best set of execution plans. Synergy further improves throughput by leveraging parallelization opportunities over multiple computation units. Our evaluations with 7 baselines and 8 models demonstrate that, on average, Synergy achieves a 23.0 times improvement in throughput, while reducing latency by 73.9% and power consumption by 15.8%, compared to the baselines.