Hardware-aware Graph Neural Networks prunning for embedded event-based vision

2026-07-07 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Advanced, quick

Summary

An optimization strategy for Graph Convolutional Neural Networks (GCNNs) is proposed to adapt their architecture for embedded heterogeneous FPGA platforms, which are crucial for event-based cameras in mobile robotics requiring efficient real-time processing. This method integrates hardware-aware pruning and quantization, carefully balancing on-chip memory savings against inference accuracy. The design space is strategically explored using Fine Grid Search and Greedy layer-wise Iterative Deepening Search. Evaluations demonstrated significant BRAM memory reductions: 28.8% for CIFAR-10 (with a 1.65% accuracy decrease), 31.4% for MNIST-DVS (3.55% accuracy drop), and 26.5% for N-Caltech101 (5.18% accuracy reduction) across various network configurations and datasets.

Key takeaway

For AI Hardware Engineers deploying Graph Convolutional Neural Networks on resource-constrained FPGA platforms for event-based vision, this hardware-aware pruning and quantization strategy provides a critical method to reduce on-chip memory. You can achieve substantial BRAM memory savings, such as 28.8% for CIFAR-10, while managing acceptable accuracy trade-offs. Consider integrating these design space exploration techniques to optimize your GCNN architectures for specific embedded targets.

Key insights

Hardware-aware pruning and quantization optimize GCNNs for embedded FPGAs, balancing memory and accuracy.

Principles

• Prioritize hardware constraints in model optimization.
• Balance memory savings with accuracy trade-offs.
• Utilize design space exploration for adaptation.

Method

The approach uses hardware-aware pruning and quantization, guided by Fine Grid Search and Greedy layer-wise Iterative Deepening Search, to adapt GCNNs to FPGA resources.

In practice

• Apply pruning to reduce BRAM memory on FPGAs.
• Quantize GCNNs for embedded platforms.
• Use grid/iterative search for architecture tuning.

Topics

• Graph Neural Networks
• Hardware-aware Pruning
• Event-based Vision
• FPGA Platforms
• Model Quantization
• Embedded Systems

Best for: AI Scientist, Research Scientist, AI Hardware Engineer, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.