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Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System
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DOI：10.3389/fnins.2019.00754.png)
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Abstract
En 中文
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:
sparsity
sparse-approximation
sparse-code
brain-inspired
TrueNordi
spiking-neurons
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