Adaptive Intelligence for Batteryless Sensors Using Software-Accelerated Tsetlin Machines

Tsetlin Machine (TM) is a new machine learning algorithm that encodes propositional logic into learning automata (a set of logical expressions composed of boolean input features) to recognise patterns. The simplicity, efficiency, and accuracy of this logic-based algorithm encourage rethinking the application of traditional arithmetic-based neural networks (NNs) in intelligent sensors design. Indeed, TM is a promising candidate for embedding intelligence into tiny batteryless sensors with the potential to address two critical challenges: (1) computing under resource constraints and (2) demand for dynamic adaptation to the unpredictable nature of harvested energy. However, its structural model complexity (i.e., an increase in boolean features leads to larger TM size) manifests in two conflicting issues: large memory footprint and long latency. This paper addresses these shortcomings by proposing adaptive compression techniques exploiting the inherent redundancies observed in trained models. Through dynamically scaling the computational complexity based on available energy, our techniques significantly reduce the memory footprint and speed up the runtime execution. We evaluate our techniques against standard TMs and binarized neural networks (BNNs) for vision and acoustic workloads deployed on a TI MSP430 MCU operating under intermittent power supply conditions. We show that our techniques can achieve up to 99% compression on standard TM implementations and offer 13.5x latency and energy reductions when compared with the most efficient neural network configuration without compromising accuracy.