On-Chip Gradient Boosted Tree Classifier for Medical Sensors
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Solution Overview
Problem
Existing machine learning technologies face challenges in implementing gradient boosted trees in applications with stringent power, area, and delay constraints, such as medical devices, where deep learning techniques are not practical due to limited computational resources and time constraints.
Innovation Solution
An efficient hardware architecture is developed to implement gradient boosted trees, utilizing asynchronous tree operation and sequential feature extraction, with a programmable FIR filter and multiplexer to select and process only necessary input channels, reducing power consumption and hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques are used for classification, then classification accuracy is improved, but power consumption and hardware complexity increase beyond available resources
Solution Approach 1:
The patent replaces complex deep learning models with simpler decision tree structures that are easier and cheaper to implement in hardware. The decision trees are trained offline and deployed as static hardware circuits, eliminating the need for continuous complex computational resources while maintaining acceptable classification accuracy for the specific medical application.
Solution Approach 2:
The patent transforms the classification approach by changing the fundamental parameters of the computational model - moving from neural networks with millions of parameters to decision trees with a manageable number of nodes and branches. This parameter transformation enables hardware implementation within the constrained power and area budgets of medical devices.
2Measurement precision
If deep learning techniques are used for classification, then classification accuracy is improved, but hardware area and computational resources exceed available constraints
Solution Approach 1:
The patent replaces complex deep learning models with simpler decision tree structures that are easier and cheaper to implement in hardware. The decision trees are trained offline and deployed as static hardware circuits, eliminating the need for continuous complex computational resources while maintaining acceptable classification accuracy for the specific medical application.
Solution Approach 2:
The patent segments the classification task into discrete decision nodes and branches that can be independently implemented in hardware. Each node represents a simple threshold comparison on a specific feature, and each branch leads to a classification decision. This segmentation allows the complex classification problem to be broken down into manageable hardware components that fit within area constraints.
3Reliability
If all input channels are processed, then classification completeness is improved, but power consumption and processing time increase
Solution Approach 1:
The patent applies partial action by processing only the subset of input channels and features that are actually needed for the classification decision at each node of the decision tree. Rather than continuously monitoring all input channels, the system selectively processes only those channels that are relevant to the current decision path, reducing power consumption while maintaining classification reliability.
Solution Approach 2:
The patent implements local quality by making the processing behavior adaptive to the specific decision context. Different nodes in the decision tree may process different subsets of input channels depending on the feature importance and the current state of processing. This allows the system to concentrate computational resources on the most critical features at each decision point rather than uniformly processing all inputs.
4Productivity
If gradient boosted trees are implemented with standard hardware architecture, then computational capability is improved, but power consumption and delay increase under stringent constraints
Solution Approach 1:
The patent replaces the standard von Neumann architecture with a parallel hardware implementation of decision trees. Instead of using a general-purpose processor that sequentially executes instructions, the system uses dedicated hardware circuits that simultaneously evaluate multiple decision nodes in parallel, dramatically reducing computational delay and power consumption for the same classification task.
Solution Approach 2:
The patent applies preliminary action by training the decision trees offline using comprehensive datasets and computational resources, then deploying the trained models as fixed hardware circuits. This separates the computationally intensive training phase from the deployment phase, allowing the hardware to execute classification decisions quickly and efficiently without the overhead of runtime computation.
Data Source
AI summary
Methods, systems, and devices are disclosed for an efficient hardware architecture to implement gradient boosted trees for detecting biological conditions. For example, a method of detecting a biological condition includes receiving, by a device, a plurality of physiological signals from a plurality of input channels of the device, selecting, based on a trained prediction model, one or more input channels from the plurality of input channels, converting the one or more physiological signals received from the one or more input channels to one or more digital physiological signals, identifying, by using the plurality of gradient boosted decision trees, the selected characteristic in the one or more digital physiological signals, and determining a presence of a physiological condition based on an addition of the output values obtained from the plurality of gradient boosted decision trees.