Hi

What if you could predict a critical machine failure—weeks before it happens—saving millions in downtime and repairs?

This isn't a fantasy. It's what happens when you deploy TinyML on ultra-low-power RISC-V chips at the edge.

In our hands-on workshop, you won't just learn theory. You'll build a real-world predictive maintenance system. Here’s how:

• Train ML models to recognize patterns of failure (like abnormal vibrations or temperatures) from sensor data.
• Quantize and optimize these models to run on a RISC-V chip (VSDSquadron) with severe memory constraints (<16KB RAM).
• Deploy your model to perform real-time inference on the device, triggering alerts before a failure occurs.
• No cloud needed. No expensive infrastructure. Just intelligent, efficient, and reliable edge computing.

This is the skill set that companies in manufacturing, energy, and IoT are desperately searching for.

Ready to move from concepts to building market-ready, industrial-grade solutions?

Enroll Now: Edge AI on RISC-V

Transform your resume. Build the future.

Best regards,
The VLSI System Design Team