FPGA-Based Ensemble Neural Networks

Problem

Traditional neural network inference on CPUs and GPUs faces latency and power consumption challenges for real-time applications. Hardware acceleration using FPGAs offers a promising solution, but implementing complex ensemble networks on FPGA hardware requires careful optimization.

Approach

I designed and implemented an ensemble neural network architecture optimized for FPGA deployment. The system combines multiple lightweight neural network models running in parallel on FPGA hardware, with a voting mechanism for final predictions.

Architecture

The system consists of:

• Multiple neural network models trained in TensorFlow
• Hardware description in Verilog for FPGA implementation
• Ensemble voting mechanism for improved accuracy
• Real-time inference pipeline

Key Design Decisions

1. Model Selection: Chose lightweight architectures that balance accuracy and hardware resource usage
2. Parallel Processing: Implemented parallel execution of multiple models on FPGA
3. Quantization: Applied model quantization to reduce memory footprint
4. Pipeline Optimization: Designed efficient data flow to minimize latency

Results

• Performance: Achieved real-time inference at 100 FPS
• Accuracy: 95% ensemble accuracy, outperforming individual models
• Efficiency: Significant reduction in power consumption compared to GPU-based solutions
• Research Impact: Contributed to hardware-accelerated ML research

Learnings

This project taught me:

• Deep understanding of FPGA architecture and Verilog programming
• Neural network quantization and optimization techniques
• Hardware-software co-design principles
• The importance of balancing accuracy and resource constraints in embedded ML systems