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

Frontiers in Neuroscience

Andreas G. Andreou¹  Daniel R. Mendat¹  David V. Anderson²  Kaitlin L. Fair²  Christopher J. Rozell²  Justin Romberg² 

[1] Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States;School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States;

关键词: sparsity;    sparse-approximation;    sparse-code;    brain-inspired;    TrueNorth;    spiking-neurons;   

DOI  :  10.3389/fnins.2019.00754

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown