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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
An Embedded RISC-V Evaluation of Kolmogorov--Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models
TL;DR
This study evaluates the performance of Kolmogorov--Arnold Networks (KANs) as replacements for multilayer perceptrons (MLPs) in hard-constrained recurrent physics-informed models. Results show that KANs are significantly slower and consume more energy than MLPs when deployed on embedded RISC-V platforms.
✦ Why It Matters
Consider using multilayer perceptrons for embedded HRPINN deployments to ensure better performance and reliability.
Key Takeaways
- Evaluate the use of MLPs over KANs for embedded systems.
- Measure execution latency and energy consumption before deployment.
- Consider quantization strategies to improve KAN performance.
- Test models on RISC-V platforms to assess real-world efficiency.
How It Works
HRPINNs combine physical laws with neural networks to model dynamics. KANs utilize learnable B-spline activations to capture residual dynamics, but their execution differs significantly from MLPs.
⚠ The Catch
KANs demonstrated significantly higher execution times and energy consumption compared to MLPs, raising concerns about their practical deployment in embedded systems.