Nanosecond BDT Evaluation on FPGA Using Tree Flattening
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Solution Overview
Problem
Existing machine learning algorithms, particularly boosted decision trees (BDT) and autoencoders, face challenges in achieving low latency and efficient resource usage on Field Programmable Gate Arrays (FPGAs), and there is a need for accurate and efficient anomaly detection and data transmission in high-rate, high-efficiency environments like the Large Hadron Collider.
Innovation Solution
A system and method for optimizing BDT evaluation on FPGAs using a nanosecond optimizer that includes tools like tree flatteners, forest mergers, score normalizers, and converters to produce hardware description languages for optimized firmware implementation, along with autoencoders using decision tree grids for nanosecond anomaly detection and data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning algorithms (BDT, autoencoders) are implemented on FPGA for real-time processing, then processing speed and latency are improved, but device complexity and resource usage increase
Solution Approach 1:
The patent segments the machine learning algorithms into discrete hardware modules including decision tree engines, autoencoder blocks, and lookup tables that can be independently instantiated and configured on FPGA. This modular segmentation allows the system to achieve high processing speeds through parallel execution while managing device complexity through organized, reusable components.
Solution Approach 2:
The patent uses copying by implementing pre-trained model parameters and decision tree structures as static lookup tables and configuration data on the FPGA. This allows the hardware to execute predictions at nanosecond speeds by copying stored decision logic and weight values into fast access memory, avoiding complex real-time computations while maintaining high processing speed.
2Loss of time
If BDT evaluation is performed in nanoseconds on FPGA, then latency is reduced, but resource usage increases
Solution Approach 1:
The patent applies preliminary action by pre-training the decision trees and autoencoders offline, then storing the trained parameters, thresholds, and decision logic in FPGA memory before deployment. This preliminary preparation allows the hardware to perform rapid nanosecond-latency evaluations by simply evaluating pre-computed decision paths without performing complex training or optimization during real-time operation, thus reducing latency while controlling resource usage.
Solution Approach 2:
The patent changes parameters by optimizing the precision and format of stored model parameters for FPGA implementation. It uses fixed-point arithmetic instead of floating-point, quantizes weights and thresholds to appropriate bit-widths, and structures decision tree outputs to match hardware capabilities. These parameter changes enable nanosecond-latency evaluation while minimizing the quantity of resources required for storage and computation.
3Measurement precision
If autoencoders are used for anomaly detection in high-rate environments, then detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent substitutes the mechanical system by replacing complex autoencoder neural network computations with equivalent decision tree logic and lookup table evaluations on FPGA. This substitution maintains detection accuracy by training decision trees to replicate autoencoder anomaly detection behavior, while dramatically reducing processing complexity through hardware-accelerated parallel evaluation of simplified decision paths.
4Measurement precision
If multiple decision trees are merged into a forest for better classification, then classification accuracy is improved, but evaluation time increases
Solution Approach 1:
The patent merges multiple decision trees into a forest structure where all trees are evaluated in parallel on the FPGA hardware. The individual tree outputs are then combined through a voting or averaging mechanism to produce the final classification. This merging approach improves classification accuracy by combining multiple weak learners while the parallel hardware evaluation prevents evaluation time from increasing proportionally with the number of trees.
Data Source
AI summary
A system for providing a boosted decision tree (BDT) for use on an electronic device to provide an event score based on a user input event, where the device includes: a machine learning trainer configured to create a trained BDT from an untrained BDT by determining parameters for the untrained BDT; a nanosecond optimizer configured to create an optimized BDT, the nanosecond optimizer including at least one of a tree flattener, a tree merger, a score normalizer, a tree remover, and a cut eraser; and a converter coupled to the nanosecond optimizer and configured to receive the optimized BDT from the nanosecond optimizer and convert the optimized BDT to a language for high-level-synthesis to produce a hardware description language representation of the optimized BDT, wherein the hardware description language representation of the optimized BDT is structured and configured to be implemented in firmware provided on the electronic device.