Mini Tutorials ISCAS 2019

Mini Tutorials ISCAS 2019

Deep/Binarized NNs Learning on FPGAs/ASICs: Current Efforts

  • Osman Elgawi, SQU

Abstract:

As a popular machine learning (ML) model, deep neural networks (DNNs) continue to inspire new development of on-chip intelligence for wide range of emerging applications including vision, unmanned vehicle, consumer electronics, and the IoT devices, due to their non-linear characteristics, and self adaptive features. ML techniques, however, require enormous computational power, which is not always available on embedded systems due to their serve resource constraints. "For instance, popular DNNs such as VGGNet and ResNet, contain over 140 million floating-point (FP) parameters and performs over 15 billion FP operations to classify one image”. Customized and reconfigurable hardware such as ASICs and FPGAs can offer significant improvements in energy efficiency and power dissipation while speeding up computations, and providing high performance computing (HPC) at minimal cost power consumption. This mini-tutorial is to present current efforts of on-chip intelligence, and identify open problems in designing future ultra-low power and secure hardware.

Biographies

  • Osman Elgawi

    has been –since August 2016 with the department of Electrical and Computer Engineering at SQU. His research interests are in low-power integrated circuits and system design, energy-aware signal processing microelectronics, and machine learning on FPGAs/ASICs. The significance of his research activity has been reflected in many top-tier venues, including CVPR-W, ICRA, IEEE-SMC, IJCNN, etc. He has given numerous keynote/invited lectures and talks internationally. He was the recipient of the Japanese government (MEXT) Fellowship for postgraduate study in Japan. Elgawi received his PhD degree in Engineering from Tokyo Institute of Technology and completed PhD course in University of Tokyo, and has been a visiting researcher at University of Birmingham, UK and UCLA, USA.