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

Kaitlin L Fair1, Daniel R Mendat2, Andreas G Andreou2

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Summary
This summary is machine-generated.

The Locally Competitive Algorithm (LCA) was mapped onto the IBM TrueNorth chip for efficient sparse coding. This brain-inspired hardware implementation shows promising results comparable to traditional computing methods.

Keywords:
TrueNorthbrain-inspiredsparse-approximationsparse-codesparsityspiking-neurons

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Area of Science:

  • Computational Neuroscience
  • Neuromorphic Engineering
  • Artificial Intelligence

Background:

  • Sparse coding is crucial for efficient neural signal representation.
  • The Locally Competitive Algorithm (LCA) offers a biologically plausible model for sparse coding.
  • Neuromorphic systems like IBM TrueNorth aim to mimic brain functionality.

Purpose of the Study:

  • To implement the LCA algorithm on the IBM TrueNorth neurosynaptic system.
  • To investigate the feasibility and performance of LCA on neuromorphic hardware.
  • To analyze data structures, functional units, and micro-architectural designs for LCA on TrueNorth.

Main Methods:

  • Mapping the LCA algorithm onto the TrueNorth architecture.
  • Designing data structures and functional processing units for vector-matrix multiplication and non-linear thresholding.
  • Implementing dynamical iterative algorithms within the micro-architecture.
  • Conducting experimental comparisons using fixed-point arithmetic on TrueNorth versus floating-point on general-purpose computers.

Main Results:

  • Successful implementation of LCA on the IBM TrueNorth system.
  • Favorable comparison of LCA performance using limited-precision fixed-point arithmetic against standard floating-point computations.
  • Analysis of the algorithm's scalability within TrueNorth's constraints.

Conclusions:

  • The LCA algorithm can be effectively implemented on neuromorphic hardware like IBM TrueNorth.
  • Neuromorphic implementation offers a viable and efficient approach for sparse coding.
  • This work demonstrates the potential of brain-inspired computing for complex algorithms.