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

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

14 اقتباسات (Scopus)


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.

اللغة الأصليةEnglish
الصفحات (من إلى)71-95
عدد الصفحات25
دوريةIPSJ Transactions on System LSI Design Methodology
مستوى الصوت5
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2012
منشور خارجيًانعم

ASJC Scopus subject areas

  • ???subjectarea.asjc.1700.1706???
  • ???subjectarea.asjc.2200.2208???


أدرس بدقة موضوعات البحث “System-on-chip for biologically inspired vision applications'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا