There has been tremendous growth and advancements in deploying AI algorithms on the Edge devices, UAVs or surveillance cameras and platforms, to make stand-alone devices and gadgets intelligent. Local and embedded machine learning (ML) is a key component for real-time data analytics in upcoming computing environments like the Internet of Things (IoT), edge computing and mobile ubiquitous systems. There is an increasing need for real-time intelligent data analytics, driven by a world of Big Data, and the society’s need for pervasive intelligent devices, such as wearables for health and recreational purposes, smart city infrastructure, e-commerce, Industry 4.0 and autonomous robots. With huge volumes of data comes memory issues, privacy constraints, incompleteness and uncertainty. As a result, we observe a strong need for new ML methods to address the requirements of emerging workloads deployed in the real-world, such as uncertainty, robustness, and limited data. In order to not hinder the deployment of such methods on various computing devices, and to address the gap in between application and computer hardware, we furthermore need a variety of tools and first of these tools are Edge devices.
This hands-on workshop will cover key aspects of modern programmable ML-on-Chip and its applications to advanced scientific instrumentation and reconfigurable computing. It is based on FPGA and dual-core processors are characterized by its low-cost along with a huge versatility to implement different concurrent tasks such as high performance multichannel data acquisition, processing and transmission.
Who Should Attend?
- Industry professionals
- Graduate and Undergraduate students
- Business leaders
- Academics looking for inter-disciplinary research opportunities