Presentation
Energyhdc: Intermittent Hyperdimensional Inference on Energy-Harvesting IoT
DescriptionAmbient energy harvesting technologies offer the promise of perpetual operation for batteryless Internet of Things (IoT) devices; however, their execution is frequently halted by unpredictable power failures. Traditional methods to ensure correctness, such as software checkpointing (e.g., Mementos) and newer systems for deep neural networks (e.g., DynBal), are burdened by substantial energy and latency overheads for progress preservation, limiting their use on ultra-low-power platforms. This paper introduces EnergyHDC, an energy-aware inference system designed for intermittent operation by integrating Hyperdimensional Computing (HDC). The system pairs HDC's algorithmic robustness with fine-grained energy adaptation using three key contributions: (1) an energy-proportional checkpointing that optimizes preservation granularity against available energy; (2) a dimension-first reordering that slashes per-checkpoint storage; and (3) a priority-based pruning that allows for compile-time energy-accuracy trade-offs. Evaluated on an MSP432P401R microcontroller platform under intermittent power, EnergyHDC demonstrates up to 214x speedup compared to Mementos. It also achieves 19.6x faster inference than DynBal under similar accuracy and realistic energy-harvesting conditions. These results validate that a co-design approach, coupling energy-aware execution with HDC's intrinsic robustness, can reframe intermittence from a system constraint into an opportunity for efficient edge intelligence.
Event Type
Research Manuscript
TimeTuesday, July 2811:23am - 11:36am PDT
LocationMtg Room 202C
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