HLS (High-Level Synthesis) has the unique ability to go from complex algorithms written in C to RTL enabling accurate profiles for power and performance for an algorithm's implementation without having to write it by hand. Neural Networks are typically developed and trained in a high-performance compute environment but in many cases, the inference solution can be reduced and then HW accelerators are the only solution to meet power and real-time requirements. This session reviews the consideration around fast HW prototyping for validating acceleration in Neural Networks for Inferencing vs highest performance implementation and the tradeoffs.
This webinar is part 4 of the seminar Rapid Algorithm to HW: Using HLS for Computer Vision and Deep Learning.
What you will learn:
How HLS can be used to implement an example Computer Vision Algorithm in either an FPGA or ASIC technology and the trade-offs for power and performance. You will walk away with examples, building blocks, etc that are completed and can be referenced.
How to achieve faster but complete verification signoff in an HLS flow measuring quality, coverage; saving days and weeks in verification costs
How HLS can be applied in multiple ways to implement acceleration for Deep Learning and in particular Convolutional Neural Networks. You will walk away with examples, building blocks, etc that are completed and can be referenced.
Who should view:
RTL Designers or Project Managers interested in moving up to HLS
Architects or Algorithm developers in the field of image processing, computer vision, machine and deep learning interested in rapid and accurate exploration of power/performance metrics
New Project teams with only a few designers and multiple SW experts wanting to rapidly create high-performance FPGA or ASIC IP for Computer Vision or Deep Learning markets
Meet the speaker
Michael Fingeroff has worked as an HLS Technologist for the Catapult High-Level Synthesis Platform at Siemens Digital Industries Software since 2002. His areas of interest include Machine Learning, DSP, and high-performance video hardware. Prior to working for Siemens Digital Industries Software, he worked as a hardware design engineer developing real-time broadband video systems. Mike Fingeroff received both his bachelor's and master's degrees in electrical engineering from Temple University in 1990 and 1995 respectively.