Invited Talk Abstract: Challenges and Solutions for Embedding Vision AI

Recently computer vision and neural network based AI technology have seen explosive demands in embedded systems such as robots, drones, autonomous vehicles, etc. Due to cost and power constraints, it remains quite challenging to achieve satisfactory performance, while maintaining power efficiency and scalability for embedded vision AI. This presentation first analyzes the technical challenges of embedding vision AI, from the perspectives of algorithm complexity, computation and memory BW demands, and constrains of power consumption profile. The analysis shows that modern neural networks for vision AI contain complex topology and diversified computation steps. These neural networks are often part of a large embedded vision processing pipeline, intermixed with conventional vision algorithms. As a result, the vision AI implementation demands several TOPS computation performance and ten's of GB memory BW. Subsequently the architecture of Tensilica Vision AI DSP processor technology is presented with three distinctive advantages: The optimized instruction sets of Vision P6 and Vision C5 DSP are explained as examples of achieving instruction level computation efficiency and performance. This is coupled with unique processor architecture features for achieving SoC level data processing efficiency and scalability that lead to a high-performance vision AI sub-system. The fully automated AI optimization framework, software libraries and tools provide practical performance tuning methodology and rapid turn-around time for embedded vision AI system design. In conclusion, the presentation offers considerations for future research and development to bring embedded vision AI to the next performance level.