Solving QUBO on the Loihi 2 Neuromorphic Processor
This work addresses energy-efficient computing for edge applications, but it is incremental as it adapts an existing method to a new hardware platform.
The authors tackled the problem of solving Quadratic Unconstrained Binary Optimization (QUBO) by developing an algorithm for the Intel Loihi 2 neuromorphic processor, achieving feasible solutions in as little as 1 ms and up to 37x more energy efficiency compared to CPU baselines.
In this article, we describe an algorithm for solving Quadratic Unconstrained Binary Optimization problems on the Intel Loihi 2 neuromorphic processor. The solver is based on a hardware-aware fine-grained parallel simulated annealing algorithm developed for Intel's neuromorphic research chip Loihi 2. Preliminary results show that our approach can generate feasible solutions in as little as 1 ms and up to 37x more energy efficient compared to two baseline solvers running on a CPU. These advantages could be especially relevant for size-, weight-, and power-constrained edge computing applications.