Reconfigurable Random Forest Circuits for Agile Tree Updates
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Solution Overview
Problem
Autonomous vehicles require agile and retrainable classification mechanisms, such as random forests, to detect changing behaviors for improved safety and performance, but existing hardware solutions lack the flexibility to adapt to mutating tree structures efficiently.
Innovation Solution
The implementation of low-latency, fully reconfigurable hardware logic for ensemble classification methods, allowing dynamic reconfiguration of decision trees and interconnects to optimize feature data routing and processing, enabling parallel execution and efficient reprogramming of hardware components based on configuration changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hardware classification mechanisms are made reconfigurable to adapt to changing classification requirements, then adaptability is improved, but device complexity increases
Solution Approach 1:
The random forest classifier is segmented into multiple independent decision trees, each tree into nodes, and each node into configurable parameters. This hierarchical segmentation allows selective reconfiguration of individual trees or nodes without affecting the entire system, reducing the complexity burden of full reconfigurability while maintaining adaptability.
Solution Approach 2:
The hardware architecture implements universal decision tree nodes that can perform multiple classification operations through configurable parameters. Each node is designed to handle different feature comparisons and decision logic, allowing the same hardware structure to serve multiple classification functions, thus improving adaptability without proportionally increasing device complexity.
2Adaptability or versatility
If hardware is reprogrammed to reflect changing classification mechanisms, then adaptability is improved, but loss of time occurs during reconfiguration
Solution Approach 1:
Decision trees are pre-configured with default classification parameters and structures during system initialization or offline training phases. When adaptation is needed, only specific parameters or individual trees require reconfiguration rather than the entire system, significantly reducing the time loss during runtime reprogramming while maintaining adaptability to changing classification requirements.
3Productivity
If fully parallel hardware execution is implemented for random forests, then productivity is improved, but device complexity increases
Solution Approach 1:
The parallel execution architecture segments the random forest into independent decision trees that can execute simultaneously. Each tree is further segmented into nodes that process different feature comparisons in parallel. This segmentation enables high productivity through parallel processing while managing device complexity by using standardized, reusable node modules rather than custom complex circuits for each tree.
Solution Approach 2:
The hardware architecture implements a nested structure where decision trees are nested within the random forest ensemble, and nodes are nested within trees. This nesting allows hierarchical parallel execution where multiple trees operate in parallel at the ensemble level, while nodes within each tree also execute in parallel, maximizing productivity without requiring a monolithic complex design.
Data Source
AI summary
Systems, methods, computer program products, and apparatuses for low latency, fully reconfigurable hardware logic for ensemble classification methods, such as random forests. An apparatus may comprise circuitry for an interconnect and circuitry for a random forest implemented in hardware. The random forest comprising a plurality of decision trees connected via the interconnect, each decision tree comprising a plurality of nodes connected via the interconnect. A first decision tree of the plurality of decision trees comprising a first node of the plurality of nodes to: receive a plurality of elements of feature data via the interconnect, select a first element of feature data, of the plurality of elements of feature data, based on a configuration of the first node, and generate an output based on the first element of feature data, an operation, and a reference value, the operation and reference value specified in the configuration of the first node.