  Due to the high energy consumption and scalability challenges of deep
learning, there is a critical need to shift research focus towards dealing with
energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to
machine learning that has demonstrated significantly reduced energy usage
compared to neural networks alike, while performing competitively accuracy-wise
on several benchmarks. However, TMs rely heavily on energy-costly random number
generation to stochastically guide a team of Tsetlin Automata to a Nash
Equilibrium of the TM game. In this paper, we propose a novel finite-state
learning automaton that can replace the Tsetlin Automata in TM learning, for
increased determinism. The new automaton uses multi-step deterministic state
jumps to reinforce sub-patterns. Simultaneously, flipping a coin to skip every
$d$'th state update ensures diversification by randomization. The $d$-parameter
thus allows the degree of randomization to be finely controlled. E.g., $d=1$
makes every update random and $d=\infty$ makes the automaton completely
deterministic. Our empirical results show that, overall, only substantial
degrees of determinism reduces accuracy. Energy-wise, random number generation
constitutes switching energy consumption of the TM, saving up to 11 mW power
for larger datasets with high $d$ values. We can thus use the new $d$-parameter
to trade off accuracy against energy consumption, to facilitate low-energy
machine learning.

Machine Learning;Artificial Intelligence