Alien vs. Predator: Brain Inspired Sparse Coding Optimization on Neuromorphic and Quantum Devices

Abstract

Machine Learning has achieved immense progress by exploiting CPUs and GPUs on classical computing hardware. However, the inevitable end of Moore's Law on these devices requires the adaptation and exploration of novel computational platforms in order to continue these advancements. Biologically accurate, energy efficient neuromorphic systems and fully en-tangled quantum systems are particularly promising arenas for enabling future advances. In this work, we perform a detailed comparison on a level playing field between these two novel substrates by applying them to an identical challenge.We solve the sparse coding problem using the biologically inspired Locally Competitive Algorithm (LCA) on the D-Wave quantum annealer and Intel Loihi neuromorphic spiking processor. The Fashion-MNIST data set was chosen and dimensionally-reduced by sparse Principal Component Analysis (sPCA). A sign flipped second data set was created and appended to the original in order to give each class a mean zero distribution, effectively creating an environment where the data could not be linearly separated. An early in time normalization technique for Loihi is presented along with analysis of optimal parameter selection and unsupervised dictionary learning for all three variations. Studies are ongoing, but preliminary results suggest each computational substrate requires casting the NP-Hard optimization problem in a slightly different manner to best capture the individual strengths, and the new Loihi method allows for more realistic comparison between the two.