Automata Processors for Space-Efficient Random Decision Forests
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Solution Overview
Problem
Existing Von Neumann processor architectures are memory-bound when executing random decision forest models, leading to limited success in optimizing processing and performance efficiency for machine learning computations.
Innovation Solution
Implementing random decision forest models using automata processors that convert tree-based ensemble models into pipelined designs, representing feature values as symbol sets and using state transition elements to process feature identification chains, and applying compression techniques to achieve space-efficiency on limited automata processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random decision forest models are executed on Von Neumann processor architectures, then the models can be implemented using standard computing hardware, but the execution becomes memory-bound and processing efficiency is limited
Solution Approach 1:
The patent segments the random decision forest model into multiple parallel processing components including decision tree ensembles, feature identification chains, and state transition elements. Each segment can be processed independently and in parallel, reducing memory access bottlenecks and improving overall processing efficiency by distributing computational load across multiple processing units
Solution Approach 2:
The patent introduces specialized intermediaries including automata processors, state transition elements, and feature identification chains that act as mediators between raw input data and final classification results. These intermediaries pre-process and organize data in ways that reduce random memory access patterns, enabling more efficient data flow and computation
2Productivity
If tree-based ensemble models are converted into pipelined designs with state transition elements, then processing efficiency improves, but the device complexity and space requirements increase
Solution Approach 1:
The patent merges multiple functional components into integrated automata processor structures where state transition elements, feature identification chains, and decision logic are combined into unified processing units. This merging reduces the overall number of discrete components needed while maintaining the pipelined processing architecture, thereby improving prediction throughput without proportionally increasing device complexity
Solution Approach 2:
The patent designs universal state transition elements that can handle multiple functions including feature identification, decision logic execution, and state management within a single processing unit. This multi-functionality reduces the number of specialized components required, simplifying the overall device structure while enabling complex pipelined processing of random decision forest models
3Quantity of substance
If compression techniques are applied to achieve space-efficiency on automata processors, then memory usage is reduced, but the implementation complexity of the compression algorithms increases
Solution Approach 1:
The patent applies preliminary compression actions during the model training and deployment phase, where decision tree ensembles and feature identification chains are pre-compressed into compact automata processor formats. This pre-compression eliminates the need for complex runtime compression algorithms, reducing memory usage while keeping implementation complexity manageable through offline preparation
Solution Approach 2:
The patent uses copying techniques to create compressed representations of decision tree structures that can be replicated and deployed across multiple automata processor units. Instead of storing full decision tree objects, compact copied representations are used that consume less memory while maintaining computational accuracy, with the copying process itself being a straightforward operation that doesn't add significant implementation complexity
Data Source
AI summary
An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and to receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.