arXiv:2606.15004v1 Announce Type: cross Abstract: Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints. Existing workflows often simplify this process along one or more axes: static proxy costs such as FLOPs or parameters, treating one MCU as representative, and continuous-inference tests instead of deployed sensing schedules. These assumptions can mis-rank Pareto-front candidates, miss infeasible deployments, and obscure schedule-dependent energy. We present CREST (Cross-platform Runtime Evalu
The proliferation of AI applications demands deployment on edge devices, making efficient hardware-in-the-loop (HIL) neural architecture search (NAS) critical for optimizing performance under tight constraints.
This research addresses a key bottleneck for deploying advanced AI on low-power embedded systems, enabling more sophisticated and autonomous edge intelligence in various applications.
Traditional NAS methods that rely on proxies or simplified tests will be less effective, as this approach emphasizes deployment-realistic evaluation, leading to better-performing and more energy-efficient embedded AI.
- · Edge AI providers
- · Microcontroller manufacturers
- · Robotics and IoT sectors
- · Embedded systems developers
- · Generic NAS methods
- · Developers ignoring hardware constraints
- · Cloud-only AI solutions
Improved performance and energy efficiency of AI models on embedded devices.
Accelerated development and adoption of AI in resource-constrained environments, leading to more distributed intelligence.
New classes of autonomous edge devices emerge, potentially reducing reliance on centralized cloud infrastructure for certain AI tasks.
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Read at arXiv cs.LG