Sparse Tree Model for Low-Power Edge Inference
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Solution Overview
Problem
Existing machine learning techniques for resource-constrained devices face challenges in achieving accurate predictions with minimal power consumption, memory usage, and latency, as they often rely on complex models that are not optimized for devices with limited resources, leading to poor prediction accuracies and high energy costs.
Innovation Solution
The implementation of a tree-based computer-implemented method that learns shallow, sparse trees with powerful nodes, using sparse matrices and joint optimization of all tree parameters and a sparse projection matrix to reduce model size and prediction costs, allowing for accurate non-linear predictions on devices with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models are used for accurate predictions, then prediction accuracy is improved, but power consumption and memory usage increase
Solution Approach 1:
The patent segments the machine learning model into a hierarchical tree structure with internal nodes and leaf nodes. Each node contains compressed parameter sets that are independently stored and processed. This segmentation allows the system to process only relevant portions of the model for each prediction, reducing overall power consumption while maintaining prediction accuracy through the distributed decision-making structure of the tree.
Solution Approach 2:
The patent extracts and stores only the essential parameter sets needed for prediction at each tree node, separating the critical model parameters from redundant information. By taking out and storing only the necessary compressed parameters in the tree structure, the system achieves accurate predictions with minimal memory usage and reduced computational overhead, directly addressing the power consumption challenge.
2Measurement precision
If complex machine learning models are used for accurate predictions, then prediction accuracy is improved, but memory usage increases
Solution Approach 1:
The patent applies local quality by assigning different parameter sets to different locations (nodes) in the tree structure. Each internal node and leaf node contains locally optimized parameter sets tailored to its specific function in the decision hierarchy. This local optimization allows the system to achieve high prediction accuracy at each node while the overall memory usage remains low because each location stores only the parameters it needs, not the entire model.
Solution Approach 2:
The patent implements a nested structure where parameter sets are organized hierarchically within the tree, with internal nodes containing parameter sets that are nested within the broader context of the full tree model. This nesting allows for efficient memory utilization where parent nodes contain aggregated parameter information that encompasses child nodes, eliminating redundancy and reducing total memory requirements while preserving prediction accuracy.
3Quantity of substance
If model size is reduced for resource-constrained devices, then power consumption and memory usage decrease, but prediction accuracy deteriorates
Solution Approach 1:
The patent introduces dynamics by making the tree structure adaptive and configurable based on available resources. The system can dynamically adjust the tree depth, number of nodes, and parameter set sizes to match the computational and memory constraints of the target device. This dynamic configuration allows the model size to be optimized for resource-constrained devices while maintaining prediction accuracy through intelligent allocation of computational resources to the most critical decision points in the tree.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for machine-learning. A method may include projecting an input feature vector of a first dimensional space into a second dimensional space to create a lower dimensional feature vector, the second dimensional space smaller than the first dimensional space, determining a first prediction vector for an internal node of the tree, determining whether to pass the first prediction vector to a first child or a second child of the internal node based on a sparse vector and the lower dimensional feature vector, determining a second prediction vector at a leaf node of the tree, and determining an overall prediction by combining the first and second prediction vectors.