A reconfigurable accelerator for neuromorphic object recognition

Jagdish Sabarad, Srinidhi Kestur, Mi Sun Park, Dharav Dantara, Vijaykrishnan Narayanan, Yang Chen, Deepak Khosla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


Advances in neuroscience have enabled researchers to develop computational models of auditory, visual and learning perceptions in the human brain. HMAX, which is a biologically inspired model of the visual cortex, has been shown to outperform standard computer vision approaches for multi-class object recognition. HMAX, while computationally demanding, can be potentially applied in various applications such as autonomous vehicle navigation, unmanned surveillance and robotics. In this paper, we present a reconfigurable hardware accelerator for the time-consuming S2 stage of the HMAX model. The accelerator leverages spatial parallelism, dedicated wide data buses with on-chip memories to provide an energy efficient solution to enable adoption into embedded systems. We present a systolic array-based architecture which includes a run-time reconfigurable convolution engine which can perform multiple variable-sized convolutions in parallel. An automation flow is described for this accelerator which can generate optimal hardware configurations for a given algorithmic specification and also perform run-time configuration and execution seamlessly. Experimental results on Virtex-6 FPGA platforms show 5X to 11X speedups and 14X to 33X higher performance-per-Watt over a CNS-based implementation on a Tesla GPU.

Original languageEnglish (US)
Title of host publicationASP-DAC 2012 - 17th Asia and South Pacific Design Automation Conference
Number of pages6
StatePublished - 2012
Event17th Asia and South Pacific Design Automation Conference, ASP-DAC 2012 - Sydney, NSW, Australia
Duration: Jan 30 2012Feb 2 2012

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC


Other17th Asia and South Pacific Design Automation Conference, ASP-DAC 2012
CitySydney, NSW

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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