Collaborative Inference via Dynamic Composition of Tiny AI Accelerators on MCUs
The advent of tiny AI accelerators opens opportunities for deep neural network deployment at the extreme edge, offering reduced latency, lower power cost, and improved privacy in on-device ML inference. Despite these advancements, challenges persist due to inherent limitations of these accelerators, such as restricted onboard memory and single-device focus. This paper introduces Synergy, a system that dynamically composes tiny AI accelerators for multi-tenant models, effectively addressing tinyML's critical challenges for the increasing demand for on-device AI. A key feature of Synergy is its virtual computing space, providing a unified, virtualized view of resources and enabling efficient task mapping to physical devices. Synergy's runtime orchestration module ensures optimal inference across dynamic and heterogeneous accelerators. Our evaluations with 7 baselines and 8 models demonstrate that Synergy improves throughput by an average of 8.0X compared to baselines.
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