Solving Boltzmann Optimization Problems with Deep Learning
This work addresses the problem of improving energy efficiency in future high-performance computing by enabling more reliable Ising-based hardware, though it appears incremental as it builds on existing machine learning techniques for optimization.
The paper tackles the challenge of optimizing circuits for Ising-based hardware, which is nondeterministic, by developing a novel machine learning approach combining deep neural networks and random forests to minimize errors in the Ising model, and provides a method to express Boltzmann probability optimization as a supervised learning problem.
Decades of exponential scaling in high performance computing (HPC) efficiency is coming to an end. Transistor based logic in complementary metal-oxide semiconductor (CMOS) technology is approaching physical limits beyond which further miniaturization will be impossible. Future HPC efficiency gains will necessarily rely on new technologies and paradigms of compute. The Ising model shows particular promise as a future framework for highly energy efficient computation. Ising systems are able to operate at energies approaching thermodynamic limits for energy consumption of computation. Ising systems can function as both logic and memory. Thus, they have the potential to significantly reduce energy costs inherent to CMOS computing by eliminating costly data movement. The challenge in creating Ising-based hardware is in optimizing useful circuits that produce correct results on fundamentally nondeterministic hardware. The contribution of this paper is a novel machine learning approach, a combination of deep neural networks and random forests, for efficiently solving optimization problems that minimize sources of error in the Ising model. In addition, we provide a process to express a Boltzmann probability optimization problem as a supervised machine learning problem.