Sparse Approximation On Energy Efficient Hardware

Physically and computationally efficient hardware coupled with fast sparse approximation solvers provide opportunities for real-time visual processing on low-power embedded platforms. This paper presents a system using the low-power Locally Competitive Algorithm (LCA) on the highly programmable, brain-inspired IBM TrueNorth chip. A small-scale spiking LCA network is successfully implemented on the TrueNorth chip, resulting in node dynamics comparable to that of a discretized LCA network. The sparse representations computed by the LCA implemented on TrueNorth result in minimal reconstruction error for every trial. This performance is achieved using only 11 of the available 4096 cores on the chip, offering the potential for scalability for real-world applications of this system.