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This paper proposes two hardware-agnostic dynamic scheduling strategies (a model-free reinforcement learning agent and an on-the-fly approximated prediction method) for managing task execution in batteryless IoT devices with unknown workloads, and evaluates them against existing approaches using a simulation framework with real-world solar data.
This opinion piece argues that AI kernel portability across different hardware (TPU, GPU, etc.) is structurally impossible due to fundamental hardware differences, and that the best AI stacks will always require hardware-specific DSLs for optimal performance, despite the industry's desire for portability.