The event for innovators who want to incorporate visual intelligence into products.
Come to Santa Clara, California to connect with hundreds of product and application developers, business leaders, investors and entrepreneurs – all focused on embedded vision. See the latest in practical technology to bring visual intelligence into cloud applications, embedded systems, mobile apps, wearables and PCs. All in one place for three full days. Hear inspiring case studies from leading innovators. See live demos of the latest enabling technologies. Dig deep into the practical applications and techniques of computer vision.
Walk away with actionable insights and know-how that you can use to bring visual intelligence into your products today.
You’re all invited — Engineers. Executives. Entrepreneurs. Investors. Media. Researchers. Analysts.
Yole Développement will participate in the following:
“AI is moving to the edge – imaging today, audio tomorrow – what is the impact on the semiconductor industry?”
By Yohann Tschudi, Software & Computing Technolgy and Market Analyst at Yole Développement
Meet us onsite!
Abstract: Artificial Intelligence (AI) is a major trend for multiple applications, disrupting industries in each one. But this brings key questions. One concerns the partitioning of whether AI hardware or software firms will benefit most from adding value. Another is whether the hardware and semiconductor content of AI systems will be based in the cloud, in the system, or at the device level. Today there is a strong and established trend to reduce amount of the calculation done on cloud hardware, and do it instead directly on user devices, which is referred to as ‘at the edge’. This trend is mainly due to cost, but also latency and data confidentiality. However, bringing calculation to device hardware must also consider important constraints: low power consumption, always-on, low latency and high performance.
Meanwhile, Moore’s law is slowing down. To further improve performance, it has become necessary to design hardware specialized to the software that runs on it. This is even truer for AI algorithms that necessitate billions of operations. While it was possible to calculate the weights of neurons in a huge network with a graphics processing unit (GPU), it was necessary to create accelerators, more commonly called neural engines, to make these millions of calculations possible on portable devices. How will this trend unfold? What kind of hardware will it need? What are the new corresponding markets that will emerge?