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Title: DeepMaker: Customizing Deep Neural Networks for COTS Programmable Devices
Abstract: Cyber Physical Systems (CPS) have become immensely prominent everywhere due to their considerable benefits including improving safety and reliability, providing higher quality of service and human’s convenience. Expanding the applicability of CFS in different aspects of life causes to emerge genesis new classes of applications such as recognition, mining, analytics, and search. Deep Machine Learning (ML) algorithms play a key role in these applications by providing high estimation accuracy. However we need to capture and process a big amount of raw data for modern applications such as autonomous system, and smart factories. Moreover, for enabling tremendous improvements in achieving accuracy, deep learning models moved toward emerging new deeper and deeper complex network architectures. Although using high-performance processing infrastructures like cloud data centers are prevalent, we aim to find a near-sensor processing solution since in some CPS we are interested in keeping processing close to sensors due to security reasons, limited network bandwidth, increasing the energy efficiency and guaranteeing worse case execution time. To reach this goal, we offer a comprehensive framework, named DeepMaker, aiming to generate a highly robust DNN accelerator for both embedded devices and high-performance cloud infrastructures. In nutshell, DeepMaker tries to bring processing close to embedded devices as much as possible.