Decision Tree Neural Network Layer Substitution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large neural networks require significant computational resources and latency, leading to inefficiencies in inference computations and memory usage, and existing techniques like sparse matrices and fusing weights have limitations such as memory latency and hardware compatibility issues.
Innovation Solution
Incorporating decision trees into large neural networks by replacing groups of layers with decision trees and quantizing inputs and outputs, allowing for reduced computational operations and memory requirements, using low-cost hardware like multiplexers instead of expensive accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large neural networks are used to improve machine learning performance, then prediction accuracy is improved, but computational resources and memory requirements increase significantly
Solution Approach 1:
The patent replaces expensive neural network layers with cheaper decision tree stumps that can be evaluated more efficiently. Decision tree stumps require only simple threshold comparisons rather than full matrix multiplications, reducing computational resource requirements while maintaining predictive capability.
Solution Approach 2:
The patent segments the neural network into groups of adjacent layers, replacing each group with a single decision tree. This segmentation allows the model to process inputs through fewer computational stages, reducing overall complexity while preserving the essential transformation capabilities of the original network.
2Reliability
If large neural networks are used to improve machine learning performance, then prediction accuracy is improved, but latency increases
Solution Approach 1:
The patent extracts and removes redundant computational operations from the neural network by replacing multiple layers with compact decision tree stumps. This extraction eliminates unnecessary computational steps that contribute to latency while retaining the core predictive functionality.
Solution Approach 2:
The patent performs preliminary quantization of input features to a limited number of bits before processing. This preliminary action reduces the precision requirements for subsequent computations, allowing the use of simpler, faster operations that reduce latency while maintaining adequate prediction accuracy.
3Productivity
If sparse matrices are used to increase computational efficiency, then memory usage is reduced, but memory latency issues arise
Solution Approach 1:
The patent substitutes the mechanical matrix multiplication operations with decision tree stump evaluations that use simple threshold comparisons. This substitution eliminates the need for memory-intensive matrix storage and access patterns, replacing them with compact decision rule evaluations that have lower memory latency.
4Productivity
If quantization is applied to reduce computational operations, then memory usage and computational cost are reduced, but precision is lost
Solution Approach 1:
The patent changes the precision parameter of input features by quantizing them to a limited number of bits. This parameter change reduces the computational complexity and memory requirements while the decision tree stump structure compensates for the reduced precision by learning optimal threshold values that maintain predictive accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods comprises receiving data representing a neural network comprising a plurality of layers arranged in a sequence; selecting one or more groups of layers each comprising one or more layers adjacent to each other in the sequence; generating a new machine learning model, comprising: for each group of layers, a respective decision tree that replaces the group of layers, wherein the respective decision tree receives as input a quantized version of the inputs to a respective first layer in the group and generates as output a quantized version of the outputs of a respective last layer in the group, wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group.