Fast Inference in Low Power Systems via CEVA’s Deep Neural Network Solution
The emergence of state-of-the-art, real-time object detection solely based on convolutional neural networks has created new and complex challenges for embedded systems. Algorithms such as Faster R-CNN, YOLO and SSD are performance-intensive and require high data bandwidth, but embedded implementations have extreme resource limitations. Various processors and hardware accelerators offer potential solutions, but entail highly complex software development and achieving optimal performance with them requires an acute understanding of how to distribute the workload across the various processing units. This presentation outlines the challenges of implementing high-precision, advanced neural networks for embedded vision, and explains how using CEVA’s automatic software tools in combination with a mix of processing units can achieve a power efficient, flexible and very fast time-to-market solution for inferencing in low-cost production systems.
Yair has been in the technology business with a focus on digital signal processors and SoCs for over 20 years. Currently he serves as CEVA’s Director of Strategic Marketing, covering business development, OEMs and partnerships, addressing audio, voice, deep learning and computer vision applications. Prior to this, he served in various roles in product marketing and field applications, as well as R&D engineering and management positions within the Software Development Tools group. Mr. Siegel holds a BSc degree in Computer Science and Economics from the Hebrew University in Jerusalem and MBA and MA degrees in Economics from Tel-Aviv University.