Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System - Fair, Kaitlin L.; Mendat, Daniel R.; Andreou, Andreas G.; Rozell, Christopher J.; Romberg, Justin; Anderson, David V. (Frontiers in Neuroscience, 2019)
The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an over-complete dictionary. In this paper we map the LCA algorithm on the brain-inspired, IBM TrueNorth Neurosynaptic System. We discuss data structures and representation as well as the architecture of functional processing units that perform non-linear threshold, vector-matrix multiplication. We also present the design of the micro-architectural units that facilitate the implementation of dynamical based iterative algorithms. Experimental results with the LCA algorithm using the limited precision, fixed-point arithmetic on TrueNorth compare favorably with results using floating-point computations on a general purpose computer. The scaling of the LCA algorithm within the constraints of the TrueNorth is also discussed.