Hi

Imagine teaching a credit card-sized microcontroller to recognize handwritten numbers - without cloud dependency or complex OS, using only bare-metal C code. We’ve achieved exactly this with the VSDSquadron PRO (SiFive FE310 RISC-V), and we’re sharing the complete blueprint for you to replicate.

Inside the Repository

GitHub Link - https://github.com/ShekharShwetank/VSDSquadron_Pro_Edge_AI_Research_Internship

This repository is your Edge-AI internship toolkit, structured in three key phases:

1. Board Bring-Up
   Start by flashing an LED blink demo to verify your hardware and toolchain work correctly.
2. Edge-AI Deployment
   Deploy a fully quantized MNIST neural network (recognizes digits 0-9) onto the board’s limited 16KB RAM using 4-bit/8-bit integer math—no floating-point operations.
3. Documentation & Debugging
   Includes board specifications, setup guides, and an error log detailing real troubleshooting (e.g., memory overruns and quantization fixes).

Think of it like training a minimalist robot to read house numbers using only a fraction of the computing power of your smartwatch.

Technical Highlights

• Bare-Metal RISC-V (RV32IMAC): Runs without an OS for maximum efficiency.
• Hardware-Agnostic Tooling: Uses open-source tools (OpenOCD, Freedom-Metal).
• Serial Output Validation: Prints prediction results directly to your laptop.

Who Should Explore This?

• Students/Interns: Hands-on learning for RISC-V, embedded C, and Edge-AI deployment.
• Educators: Turnkey lab material spanning hardware setup to AI validation.
• Developers: Evaluate ultra-low-cost inference for sensor-driven prototypes.
• Prerequisite: Fundamentals of Embedded Systems and RISC-V are required.

Limited Opportunity: Claim Your Board

• Only 9 VSDSquadron PRO boards remain in stock.
• Registration closes in 3 days (Deadline: [Date + 3 days]).
  Secure Your Board Now - https://www.vlsisystemdesign.com/vsdsquadronPro/ (Basic RISC-V and Embedded knowledge Expected)

Your Next Steps

• Validate: Clone the repo and run the LED-blink demo.
• Deploy AI: Flash the quantized MNIST model and test digit recognition.
• Extend: Add sensors or try new models (CIFAR-10) using our export scripts.

This project proves that even a 32-bit microcontroller can run practical AI—an ideal skillset for embedded careers or academic projects.

Register for Your Board - https://www.vlsisystemdesign.com/vsdsquadronPro/ (Basic RISC-V and Embedded knowledge Expected)

Best regards,
Kunal

P.S. Only 9 boards available - registration closes in 72 hours. Don’t miss out!