FPGA Neural Accelerator Kit: Build a CNN Inference Engine
Build a Custom CNN Inference Engine on Xilinx Artix-7: Beat CPU and GPU Latency with FPGA Acceleration
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Code a complete two-layer convolutional neural network inference engine in VHDL, synthesize it onto a Xilinx Artix-7 FPGA, and benchmark its latency head-to-head against CPU and GPU implementations. Designed for B.Tech ECE/EEE students and research scholars, this project bridges deep learning theory and hardware acceleration - a perfect entry point for accelerator design roles and Smart India Hackathon hardware tracks.
What You'll Build
You'll build a fully functional FPGA-based neural network accelerator that processes image data with significantly lower latency than a general-purpose processor. The output is not just a working hardware module, but a detailed comparison report showing exactly how much faster an FPGA can execute CNN inference compared to a CPU and GPU - ready for your lab submission, conference paper, or capstone project.
What You'll Learn
- Write and simulate VHDL modules for convolution, pooling, and activation layers
- Synthesize and deploy a neural network on Xilinx Artix-7 FPGA, managing timing and resource constraints
- Use an 8-channel logic analyzer to probe and validate on-chip signals during inference
- Benchmark and compare FPGA, CPU (PyTorch/TensorFlow), and GPU (CUDA) latency with real data
Kit Contents
| Component | Quantity |
|---|---|
| Xilinx Artix-7 FPGA Board | 1 |
| Logic Analyser 8CH | 1 |
| USB Cable | 1 |
| M-M Wires | 20 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
This kit is built for final-year B.Tech (ECE/EEE) students, M.Tech VLSI researchers, and participants in hardware tracks of Smart India Hackathon or DRDO innovation contests. It's also ideal for lab classrooms at IITs, NITs, VIT, BITS, and other institutions introducing FPGA-based deep learning.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I get stuck during the build?
Simply scan the QR code to open the AI companion, trained on this exact FPGA neural accelerator project. If you need further help, reach out on WhatsApp - our Bengaluru team has assisted over 15,000 builders with FPGA and VHDL debugging.
Is this kit suitable for a capstone project?
Absolutely. You'll produce a hardware accelerator and a latency comparison report that directly addresses common B.Tech and M.Tech project requirements in edge AI and reconfigurable computing.
Do I need prior FPGA experience?
Familiarity with VHDL basics is recommended, but the AI companion guides you through every synthesis step - you'll learn the workflow even if you're new to Artix-7.
Can I run larger CNNs beyond 2 layers?
The Artix-7 FPGA has limited logic; this kit teaches foundational accelerator design. You can layer multiple accelerators or upgrade to bigger devices later, but the principles scale.
Implement a 2-layer CNN inference engine in VHDL on Xilinx Artix-7 - compare FPGA vs CPU vs GPU latency.
What's in this kit
- Xilinx Artix-7 FPGA Board
- Logic Analyser 8CH
- USB Cable
- M-M Wires x20
Ask Soldr above what you can build with this — it knows every Compoden kit this part appears in.
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items