The Xelera Decision Tree Engine Demo is a high-performance inference engine designed to accelerate decision tree-based machine learning models, including Random Forest, XGBoost, and LightGBM. By leveraging FPGA (Field-Programmable Gate Array technology, it delivers ultra-low latency and high throughput, making it ideal for real-time applications where speed and efficiency are critical. Key Features and Functionality: - FPGA Acceleration: Utilizes FPGA hardware to significantly enhance the performance of decision tree inference tasks. - Broad Algorithm Support: Compatible with popular machine learning frameworks such as XGBoost, LightGBM, and Random Forest. - Seamless Integration: Offers a unified API for easy integration into existing applications, supporting both Python and C/C++ environments. - Low Latency and High Throughput: Achieves inference times as low as 1 to 3 microseconds, depending on the algorithm, ensuring rapid processing of large datasets. - Model Flexibility: Supports user-trained models, allowing dynamic deployment on accelerator cards without the need for code modifications. Primary Value and Problem Solved: The Xelera Decision Tree Engine Demo addresses the performance bottlenecks commonly associated with deploying decision tree models in latency-sensitive environments. By accelerating inference processes, it enables organizations to implement real-time decision-making capabilities in applications such as high-frequency trading, network threat detection, and other scenarios where rapid data analysis is essential. This solution not only enhances operational efficiency but also reduces the total cost of ownership by offloading computational tasks from CPUs, thereby saving server energy and freeing up resources for other critical operations.