System-on-chip for biologically inspired vision applications

Sungho Park, Ahmed Al Maashri, Kevin M. Irick, Aarti Chandrashekhar, Matthew Cotter, Nandhini Chandramoorthy, Michael Debole, Vijaykrishnan Narayanan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Neuromorphic vision algorithms are biologically-inspired computational models of the primate visual pathway. They promise robustness, high accuracy, and high energy efficiency in advanced image processing applications. Despite these potential benefits, the realization of neuromorphic algorithms typically exhibit low performance even when executed on multi-core CPU and GPU platforms. This is due to the disparity in the computational modalities prominent in these algorithms and those modalities most exploited in contemporary computer architectures. In essence, acceleration of neuromorphic algorithms requires adherence to specific computational and communicational requirements. This paper discusses these requirements and proposes a framework for mapping neuromorphic vision applications on a System-on-Chip, SoC. A neuromorphic object detection and recognition on a multi-FPGA platform is presented with performance and power efficiency comparisons to CMP and GPU implementations.

Original languageEnglish (US)
Pages (from-to)71-95
Number of pages25
JournalIPSJ Transactions on System LSI Design Methodology
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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