Solving Boltzmann Optimization Problems with Deep Learning
Fiona Knoll John T. Daly Jess J. Meyer
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.