Can you make AI models smaller, faster, and more efficient on RISC-V?
Deploying machine learning on resource-constrained edge devices comes with its challenges. But what if you could reduce model size and inference latency without compromising accuracy?
Join us for a technical webinar with Dr. Yueh-Feng Lee, R&D Software Manager at Andes Technology, to learn how to build an optimized TinyML deployment pipeline on #RISCV.
You'll see how to:
- Prepare, prune, and quantize neural network models for embedded deployment.
- Use optimized neural network kernel libraries to accelerate execution.
- Improve inference performance with the RISC-V Vector (RVV) extension.
📅 November 18, 2026
🕓 4:00–5:00 PM CET | 7:00–8:00 AM PST
Register now:
hubs.la/Q04DdLnD0
Deploying machine learning on resource-constrained edge devices comes with its challenges. But what if you could reduce model size and inference latency without compromising accuracy?
Join us for a technical webinar with Dr. Yueh-Feng Lee, R&D Software Manager at Andes Technology, to learn how to build an optimized TinyML deployment pipeline on #RISCV.
You'll see how to:
- Prepare, prune, and quantize neural network models for embedded deployment.
- Use optimized neural network kernel libraries to accelerate execution.
- Improve inference performance with the RISC-V Vector (RVV) extension.
📅 November 18, 2026
🕓 4:00–5:00 PM CET | 7:00–8:00 AM PST
Register now:
hubs.la/Q04DdLnD0