Inference Latency-Aware Tsetlin Machine Training

Abstract

The Tsetlin Machine (TM) is a logic-based machine learning model well suited to low-latency inference on resource-constrained edge devices. Because each clause is an AND over Boolean literals, both the literals and their associated Tsetlin Automaton (TA) actions can be represented at the bit level, enabling bitwise evaluation and an early exit that is triggered as soon as any included literal evaluates to zero, forcing the clause output to zero. In this work, we study how the two TM training hyperparameters, T and s, govern both classification accuracy and inference latency. Building on the inversely proportional T-s relationship along which accuracy is nearly constant, we find that inference latency is reduced primarily by increasing s; rather than exhaustively searching the (T, s) grid at high training cost, we propose a directed search strategy that treats accuracy as a constraint and advances toward larger s to reduce inference latency within a fixed evaluation budget. Experimental results on the gem5 simulator with an ARM processor show that, for certain datasets, the search is able to increase s while preserving the baseline accuracy, thereby reducing inference latency at negligible accuracy cost: across six datasets, the method reduces inference latency by up to 16.34%, while some datasets exhibit an accuracy reduction of only 0.06%. The proposed strategy requires far fewer configuration evaluations than an exhaustive grid search.