About
Reservoir Computing (RC) is a new paradigm in Machine Learning, alternative to Neural Networks on predicting dynamical systems, offering advantages in efficiency and computational simplicity. These characteristics make RC particularly well-suited for implementation on resource-constrained hardware such as FPGAs, enabling low-power, real-time edge computing. Next-Generation Reservoir Computing (NG-RC) further enhances this approach by significantly reducing the number of required parameters compared to conventional RC, making FPGA implementations even more efficient. In this work, we present an FPGA-based implementation of NG-RC for predicting the Lorenz attractor, demonstrating its effectiveness in modeling chaotic systems with minimal computational overhead..
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The talk
Credits and licence
- Institution
- CERN
- Department
- FPGA Developers' Forum (FDF)
- Speaker
- João Folhadela
- Year
- 2025
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed