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Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables

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arxiv 2401.08637 v3 pith:QAOFIAWR submitted 2023-12-11 cs.DC cs.LG

classification cs.DCcs.LG
keywords synergyappsthroughputacceleratoracceleratorsbaselinescollaborationexecution
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Test-Time Adaptation with Binary Feedback

    cs.LG 2025-05 conditional novelty 6.0 of 10

    BiTTA guides test-time model adaptation with a few binary correct/incorrect feedback labels, combining feedback-guided updates on uncertain samples with agreement-based self-adaptation on confident ones.

  2. Smaller, Smarter, Closer: The Edge of Collaborative Generative AI

    cs.DC 2025-05 conditional novelty 3.0 of 10

    Edge-first collaborative inference using small language models with cloud fallback is presented as a viable design, supported by a small experiment in which load-aware scheduling halves cloud offload costs.

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