Reconfigurable Random Forest Hardware for Low-Latency Classification
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Solution Overview
Problem
Autonomous vehicles require agile and retrainable classification mechanisms, such as random forests, which often change over time, necessitating flexible hardware implementation to adapt to mutating tree structures and improve safety and performance.
Innovation Solution
The development of low latency, fully reconfigurable hardware logic for ensemble classification methods, allowing dynamic reconfiguration of decision trees and interconnects for efficient routing of feature data, enabling parallel execution and adaptive classification based on a subset of feature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If classification mechanisms are implemented in hardware, then processing speed and decision-making latency are improved, but flexibility and retrainability are worsened
Solution Approach 1:
The patent implements dynamically reconfigurable decision tree structures where the hardware topology can be modified at runtime. Decision trees are constructed from configurable logic blocks that can be dynamically assembled and reassembled to reflect changing classification requirements, allowing the system to adapt to new data patterns while maintaining hardware acceleration benefits
Solution Approach 2:
The classification system is divided into independent decision tree modules that can be individually configured and reconfigured. Each decision tree consists of separable nodes and branches that can be independently modified, allowing partial retraining without reconfiguring the entire hardware system, thus balancing speed and flexibility
2Adaptability or versatility
If decision trees are fully reconfigurable, then adaptability to changing classification mechanisms is improved, but hardware complexity is worsened
Solution Approach 1:
The patent employs universal logic blocks that can serve multiple functions within the decision tree structure. These configurable logic units can be programmed to represent different decision nodes, branches, and leaf nodes, reducing the need for dedicated hardware for each tree component and simplifying the overall hardware architecture while maintaining full reconfigurability
Solution Approach 2:
The decision tree structure is implemented using nested hierarchical levels where smaller configurable units (nodes) are organized into larger structures (trees). This nested organization allows complex classification mechanisms to be built from simpler reusable components, managing hardware complexity through structured modularity
3Productivity
If parallel hardware execution is implemented, then processing performance is improved, but routing complexity of feature data is worsened
Solution Approach 1:
The patent pre-computes and stores optimal feature routing paths during the decision tree construction phase. Feature data streams are pre-configured with routing information that directs them through the parallel hardware execution units in the optimal sequence, eliminating the need for complex real-time routing decisions and reducing latency in parallel processing
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.