University of Pittsburgh physicists and engineers have developed a method for anomaly detection and encoded data transmission using autoencoders with decision trees that are implemented on field programmable gate arrays (FPGA). This innovative approach leverages the power of machine learning and artificial intelligence to enhance the speed and efficiency of anomaly detection and data encoding processes.
Description
The technology uses autoencoders constructed with decision trees, whose evaluations are parallelized for high efficiency, making them suitable for FPGA implementation executed at nanosecond latency. This combination of speed and efficiency allows the system to detect anomalies by comparing new data against learned patterns. Another use case of deep decision trees is for data encoding and decoding. These tools may be essential for applications in cybersecurity, financial monitoring, and industrial automation.
Applications
- Cybersecurity
- Data Transmission
- High-Performance Computing
Advantages
This novel implementation of autoencoders using decision trees offers several significant advantages. First, it achieves high-speed processing at the nanosecond level, making it ideal for real-time applications where delay could be critical. The use of decision trees over traditional neural networks reduces the computational complexity by replacing matrix multiplication with threshold comparisons, allowing for deeper models and more efficient anomaly detection for high-dimensional data. Additionally, the system's integration into FPGA provides flexibility and scalability, enabling it to be tailored to specific industry needs while maintaining a compact and efficient footprint. The ability to both detect anomalies and securely transmit data using the same system streamlines operations, reducing the need for multiple disparate systems and improving overall efficiency.
Invention Readiness
Researchers have demonstrated the system's capability to perform rapid anomaly detection with nanosecond precision, leveraging the decision trees to maintain high accuracy across a variety of datasets. The algorithm is currently being deployed at the ATLAS experiment that studies proton collisions provided by the Large Hadron Collider, located at CERN in Geneva, Switzerland.
IP Status
https://patents.google.com/patent/US20240054399A1
