Edge FPGA Neural Network Accelerator for Real-Time Image Classification
Custom Verilog CNN hardware pipeline on Xilinx Artix-7 executing matrix multiply-accumulate (MAC) units for 500+ FPS digit recognition.
Customized to Your Project Requirements
All engineering projects are custom fabricated to match your specific IEEE publication standards, academic department guidelines, and selected hardware/sensor stack. Request a custom quote to receive an itemized proposal, timeline, and deliverables list.
Project Technical Overview
Demonstrates custom silicon architecture for edge AI. Implements an 8-bit quantized convolutional neural network layer directly in hardware RTL using DSP blocks and block RAMs (BRAM), classifying incoming video camera frames with 10x lower latency than traditional microcontrollers.
Read the foundational IEEE conference research paper, citations, and algorithmic architecture for this project.