Energy-frugal and Interpretable AI Hardware Design using Learning
  Automata
Rishad Shafik Tousif Rahman Adrian Wheeldon Ole-Christoffer Granmo Alex Yakovlev
Energy efficiency is a crucial requirement for enabling powerful artificial
intelligence applications at the microedge. Hardware acceleration with frugal
architectural allocation is an effective method for reducing energy. Many
emerging applications also require the systems design to incorporate
interpretable decision models to establish responsibility and transparency. The
design needs to provision for additional resources to provide reachable states
in real-world data scenarios, defining conflicting design tradeoffs between
energy efficiency. is challenging.
  Recently a new machine learning algorithm, called the Tsetlin machine, has
been proposed. The algorithm is fundamentally based on the principles of
finite-state automata and benefits from natural logic underpinning rather than
arithmetic. In this paper, we investigate methods of energy-frugal artificial
intelligence hardware design by suitably tuning the hyperparameters, while
maintaining high learning efficacy. To demonstrate interpretability, we use
reachability and game-theoretic analysis in two simulation environments: a
SystemC model to study the bounded state transitions in the presence of
hardware faults and Nash equilibrium between states to analyze the learning
convergence. Our analyses provides the first insights into conflicting design
tradeoffs involved in energy-efficient and interpretable decision models for
this new artificial intelligence hardware architecture. We show that frugal
resource allocation coupled with systematic prodigality between randomized
reinforcements can provide decisive energy reduction while also achieving
robust and interpretable learning.