FPGA Learning Device for Gradient Boosted Decision Trees
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Solution Overview
Problem
Current machine learning technologies, particularly decision tree-based algorithms like Gradient Boosting Decision Tree (GBDT), face challenges in processing speed and accuracy, especially when dealing with large datasets and real-time applications such as robotics and high-frequency trading, due to limitations in data access latency and cache performance.
Innovation Solution
A learning device and method that utilizes a Field-Programmable Gate Array (FPGA) to improve processing speed by implementing a parallel processing architecture, allowing simultaneous access to multiple ports for feature amount calculation and branch condition derivation, thereby reducing data access latency and enhancing overall learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If processing of calculating optimum branch condition and causing sample data to branch are performed in order, then accuracy of decision tree learning is improved, but processing time is increased
Solution Approach 1:
The patent divides the learning data into multiple segments and processes each segment in parallel using separate processing units. Each unit calculates branch conditions and performs branching operations independently on its assigned segment, allowing simultaneous execution of what was previously sequential operations. This segmentation resolves the contradiction by maintaining accuracy through proper parallel processing while reducing overall processing time.
Solution Approach 2:
The patent transitions from single-threaded sequential processing to multi-threaded parallel processing by utilizing multiple processing units operating simultaneously. This dimensional change from one-dimensional sequential execution to multi-dimensional parallel execution enables both accurate branch condition calculation and reduced processing time by distributing the computational workload across multiple dimensions of processing.
2Device complexity
If sequential processing is used for calculating branch conditions and performing branching, then memory access is simplified, but processing speed is reduced
Solution Approach 1:
The patent segments learning data into multiple groups and assigns each group to a separate processing unit. Each unit has simplified memory access within its segment, while the overall system achieves high speed through parallel processing. This segmentation allows memory access to remain simple at the unit level while the aggregate system benefits from concurrent operations.
Solution Approach 2:
The patent introduces intermediate memory structures and control mechanisms that coordinate between multiple processing units. These intermediaries manage data flow and synchronization between parallel units, allowing each unit to maintain simple memory access patterns while the system as a whole achieves high processing speed through coordinated parallel operations.
3Speed
If parallel processing with multiple ports is implemented, then processing speed is improved, but device complexity is increased
Solution Approach 1:
The patent divides the processing system into multiple independent units, each with its own processing logic and memory access. This segmentation allows parallel processing to achieve high speed while keeping individual unit complexity low. The modular structure means that while the overall device is more complex, each component remains relatively simple and manageable.
Data Source
AI summary
A learning device includes: a learning unit configured to read out feature amounts of learning data from a data memory and derive a branch condition for a node of a decision tree based on the feature amounts, to perform learning of the decision tree; and a discriminator configured to perform determining, in accordance with the branch condition, a node to which learning data is to be branched from the node corresponding to the branch condition. The learning unit is configured to, in parallel with processing of the discriminator reading out learning data at a specific node from the data memory via a first port of the data memory and performing the determining, read out, from the data memory via a second port, learning data at a node on which the discriminator is configured to perform determining subsequent to the specific node and derive the branch.