Mhd Rashed Al Koutayni

"Hardware Acceleration of Deep Neural Networks"

Deep learning plays an important role in the field of computer vision, where deep neural networks (DNN) based methods are replacing the traditional vision algorithms. Due to their extensive computational requirements, these methods are implemented on Graphics Processing Units (GPUs). However, GPUs are not suitable for practical application scenarios, where low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. Our main goal is to provide energy efficient solutions for the existing computer vision algorithms. The FPGA is considered a powerful candidate, as it is highly customizable in terms of pipelining, hardware architecture and memory hierarchy. Our experiments have shown so far that our efficient FPGA implementations outweigh their GPU counterparts in terms of runtime speed and energy efficiency. The talk will present the results achieved so far as well as tools and workflows that have been developed to speedup the design process.

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