Michael Fingeroff, HLS Technologist at Mentor, presents the “Using High-level Synthesis to Bridge the Gap Between Deep Learning Frameworks and Custom Hardware Accelerators” tutorial at the May 2019 Embedded Vision Summit.
Recent years have seen an explosion in machine learning/AI algorithms with a corresponding need to use custom hardware for best performance and power efficiency. However, there is still a wide gap between algorithm creation and experimentation (using deep learning frameworks such as TensorFlow and Caffe) and custom hardware implementations in FPGAs or ASICs. In this presentation, Fingeroff explains how High-level synthesis (HLS) using standard C++ as the design language can provide an automated path to custom hardware implementations by leveraging existing APIs available in deep learning frameworks (e.g., the TensorFlow Operator C++ API).
Using these APIs can enable designers to easily plug their synthesizable C++ hardware models into deep learning frameworks to validate a given implementation. Designing using C++ and HLS not only provides the ability to quickly create AI hardware accelerators with the best power, performance and area (PPA) for a target application, but helps bridge the gap between software algorithms developed in deep learning frameworks and their corresponding hardware implementations.
