Frontiers in Neuroscience (Jul 2019)

Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System

  • Kaitlin L. Fair,
  • Daniel R. Mendat,
  • Andreas G. Andreou,
  • Christopher J. Rozell,
  • Justin Romberg,
  • David V. Anderson

DOI
https://doi.org/10.3389/fnins.2019.00754
Journal volume & issue
Vol. 13

Abstract

Read online

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.

Keywords