Exploring architectural heterogeneity in intelligent vision systems

Nandhini Chandramoorthy, Giuseppe Tagliavini, Kevin M. Irick, Antonio Pullini, Siddharth Advani, Sulaiman Al Habsi, Matthew Cotter, John Sampson, Vijaykrishnan Narayanan, Luca Benini

Abstract: Limited power budgets and the need for high performance computing have led to platform customization with a number of accelerators integrated with CMPs. In order to study customized architectures, we model four customization design points and compare their performance and energy across a number of computer vision workloads. We analyze the limitations of generic architectures and quantify the costs of increasing customization using these micro-architectural design points. This analysis leads us to develop a framework consisting of low-power multi-cores and an array of configurable micro-accelerator functional units. Using this platform, we illustrate dataflow and control processing optimizations that provide for performance gains similar to custom ASICs for a wide range of vision benchmarks.