Accelerating RNNs with Structured Matrices on FPGAs
Analysis
This article discusses the application of structured matrices to optimize Recurrent Neural Networks (RNNs) for hardware acceleration on Field-Programmable Gate Arrays (FPGAs). Such optimization can significantly improve the speed and energy efficiency of RNNs, crucial for various real-time AI applications.
Key Takeaways
- •Structured matrices are used to improve the efficiency of RNNs.
- •FPGAs are employed for hardware acceleration.
- •The approach aims to enhance both speed and energy efficiency.
Reference
“Efficient Recurrent Neural Networks using Structured Matrices in FPGAs”