CMC Microsystems is pleased to organize a one-day workshop highlighting the challenges and opportunities of AI acceleration from the cloud to the very edge. The same workshop is hosted twice on different days at different locations. You may attend either or both.
This workshop aims to bring together experts from industry and academia to share their latest achievements and innovations targeting both training and inference from cloud to edge, with a focus on:
- new architectures and approaches to accelerate deep learning (DL) workloads,
- software stack and deep learning frameworks,
- open-source processor technology RISC-V customized with ultra-low power highly-specialized computing engines for DL inference at the very edge, and
- the latest trends in AI chip design and commercialization.
- AI applications, frameworks and software stack
- Computer vision, NLP, Autonomous driving, Robotics, 5G
- Model Quantization, Pruning, Optimization, Compiler and Runtime.
- Programming models: OpenCL, OpenCV and OpenVX
- DL Inference at the edge
- Open-source processor technology (RISC-V)
- Ultra-low power highly-specialized computing engines IPs
- DL training and inference accelerators in the data center
- FPGA, GPUs and Custom Accelerators
- Impact of Future DL Models on Hardware Design
- DL Model standardization and interoperability.
- AI Chip Design and Commercialization
- Hardware/Software Co-Verification and Co-Design for FPGA and ASIC.
- CMC Microsystems infrastructure for supporting cloud and edge computing research
- CAD tools and flows for Processor design and prototyping for RISC-V and ASIPs
- FPGA/GPU cluster for machine learning
- To promote innovation, adoption and early access to advanced technologies including silicon and systems for accelerating AI workloads from cloud to the edge.
- To share insights and experiences with others; explore collaboration opportunities and connect leaders from industry to AI researchers and start-ups.
- To Influence technology selection (roadmap) and development activities of emerging AI trends.