Optimal Spike Encoding Schemes for IMU-Data: A Systematic Study
Keywords:
Delta modulation, Edge AI, Inertial measurement unit, Neuromorphic computing, Rate coding, Spike encoding, Spiking neural network, Time-to-first-spikeAbstract
Inertial Measurement Units (IMUs) produce synchronous, real-valued acceleration and angular-rate streams, whereas Spiking Neural Networks (SNNs) process sparse, discrete events. The encoder that converts IMU samples into spikes therefore shapes information fidelity, input activity, robustness, latency, and deployment energy. This study presents a systematic scoping review and theory-driven design framework for selecting spike encoders for IMU data. The review covers rate coding, Time-To-First-Spike (TTFS) coding, binary and phase coding, temporal-contrast and delta-modulation methods, population coding, burst coding, and learned spiking front ends. Because the available studies differ in datasets, preprocessing, network design, training, validation, and energy-measurement boundaries, the evidence was synthesized narratively rather than pooled statistically. The evidence does not support a universally optimal encoder. In the most directly controlled IMU comparison, distribution-aware rate coding produced the highest classification accuracy, TTFS produced the lowest input firing rate, but the poorest robustness, 10-bit binary coding produced the highest reconstruction signal-to-noise ratio and lowest reported dynamic energy, and multi-threshold delta modulation produced the smallest accuracy loss under injected spike errors. A Pareto-based objective, a decision flow, and a pre-registrable validation protocol are proposed. For dynamic embedded IMU applications, adaptive multi-threshold delta modulation is proposed as a reasoned default starting point—not as an empirically proven universal optimum—with a sparse absolute-value anchor added when static orientation or gravity information is required.
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