Synopsys AIoT Summit 2020 Resource

Introduction

This page features the presentations from both AIoT Summit Taiwan and ARC Processor Virtual Summit 2020 covering latest technologies and trends in embedded processor IP, software, programming tools and applications. You are also welcome to visit the ARC? Processor Virtual Summit and take away more in-depth information from industry leaders and Synopsys experts on the latest processor IP solutions.

Trends and Opportunities in the Era of Smart Everything

What's new on ARC/Security IPs

Yankin Tanurhan

SVP of R&D, 91吃瓜网 Group of Synopsys

Whether it’s managing the threat of a global pandemic or the pursuit of better-faster-cheaper semiconductors, tomorrow’s innovation success stories will be defined by how they overcome systemic complexity and dominate the exponential curve. Dr. Tanurhan will examine some of the trends, opportunities, and emerging IP solutions that will enable industry leaders to optimize the future of innovation, collaboration and smart everything.

Safe & Secure SoC Architectures for Auto

This presentation will describe the challenges that SoC designers and OEMs face when developing self-driving vehicles, from understanding how a pedestrian looks to software/silicon, to understanding an entire scene.

Estimating Power Early & for Smart Vision SoCs

This presentation will demonstrate how to carefully balance power-to-performance tradeoffs while meeting the requirement at today’s high-end SoCs design challenge needed to handle increasingly compute-intensive workloads.

The Future of High-Performance Embedded Processing

This presentation will look at the future structure of embedded processors that deliver the performance, scalability and flexibility needed to address the ever-increasing performance requirements for storage, automotive, networking, mobile and other high-end embedded applications.

Addressing the Challenges of RADAR and LiDAR

This presentation will go through the computation capabilities of various Synopsys ARC processors as well as discuss the use of cross computation and sensor fusion functionality to improve the quality of sensor detected object data in automotive ADAS systems.