Exploiting the error resilience of emerging data-rich applications, approximate computing promotes the introduction of small amount of inaccuracy into computing systems to achieve significant reduction in computing resources such as power, design area, runtime or energy. Successful applications for approximate computing have been demonstrated in the areas of machine learning, image processing and computer vision. In this paper we make the case for a new direction for approximate computing in the field of biometric security with a comprehensive case study of iris scanning. We devise an end-to-end flow from an input camera to the final iris encoding that produces sufficiently accurate final results despite relying on intermediate approximate computational steps. Unlike previous methods which evaluated approximate computing techniques on individual algorithms, our flow consists of a complex SW/HW pipeline of four major algorithms that eventually compute the iris encoding from input live camera feeds. In our flow, we identify overall eight approximation knobs at both the algorithmic and hardware levels to trade-off accuracy with runtime. To identify the optimal values for these knobs, we devise a novel design space exploration technique based on reinforcement learning with a recurrent neural network agent. Finally, we fully implement and test our proposed methodologies using both benchmark dataset images and live images from a camera using an FPGA-based SoC. We show that we are able to reduce the runtime of the system by 48 χ on top of an already HW accelerated design, while meeting industry-standard accuracy requirements for iris scanning systems.
