Schedule
Timezone: PDT
Cutting the AI Power Cord: Technology to Enable True Edge Inference
Kristopher ARDIS, Executive Director
Maxim Integrated
AI and deep neural networks promise to open up inventions we haven’t even dreamed of, but our best technologies to give machines the ability to see and hear are power hungry and costly. Maxim is working on new technology that will enable AI to exist at the true edge of the IoT, giving embedded devices intelligence while running off a battery.
Robert Muchsel, System Architect
Maxim Integrated
AI and deep neural networks promise to open up inventions we haven’t even dreamed of, but our best technologies to give machines the ability to see and hear are power hungry and costly. Maxim is working on new technology that will enable AI to exist at the true edge of the IoT, giving embedded devices intelligence while running off a battery.
Kristopher ARDIS, Executive Director
Maxim Integrated
Kris Ardis is an Executive Director in the Micros, Security & Software Business Unit at Maxim Integrated. He began his career with Maxim as a software engineer and holds two U.S. patents. In his current role, Ardis is responsible for Edge Artificial Intelligence accelerators, Secure and Low Power Microcontrollers, and Software Algorithms. He has a B.S. in Computer Science from the University of Texas at Austin.
Robert Muchsel, System Architect
Maxim Integrated
Robert Muchsel is the System Architect for Maxim’s new Embedded Machine Learning Accelerators. He has been with Maxim Integrated in Dallas, Texas since 2001.
With a degree in computer engineering from the Swiss Federal Institute of Technology in Zurich, Switzerland, Robert has worked on countless embedded applications and holds a variety of patents.
Timezone: PDT
GAP8: A Parallel, Ultra-low-power and flexible RISC-V based IoT Application Processor for the TinyML ecosystem
Manuele RUSCI, Embedded Machine Learning Engineer
Greenwaves technologies
In this talk, we present the GAP8 processor, a novel MCU-class IoT Application Processor equipped with a RISCV 8-core cluster for computation-intensive and parallel workloads, and the set of SW tools to speed up the development of autonomous sensors processing images and sounds (and more) at the edge. In particular, we showcase the GAPflow toolset, which is tailored for the deployment of Deep Networks on the chip and demonstrate the effectiveness of our solution on a range of applications and typical DL benchmarks.
Manuele RUSCI, Embedded Machine Learning Engineer
Greenwaves technologies
Dr. Manuele Rusci works as Embedded Machine Learning Engineer at Greenwaves Technologies. He obtained the PhD in 2018 from the University of Bologna, where he also works as a research assistant. His main research interests include low-power embedded systems and AI-powered smart sensors.
Schedule subject to change without notice.