Inflated Decision Tree Phantom Nodes Parallel Inference
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Solution Overview
Problem
Decision tree inference processing in production environments is hindered by sequential memory access patterns, leading to pipeline slowdowns due to memory access delays, which can be mitigated by offloading the workload to peripheral computational devices but requires efficient data transmission and processing.
Innovation Solution
Inflating the decision tree by inserting phantom decision nodes to create a fully-populated structure, allowing the inference accelerator to process decision nodes and leaf nodes in parallel, reducing the need for extensive parameter transmission and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decision tree inference processing is performed using conventional sequential methods, then the processing follows the natural hierarchical structure of the decision tree, but pipeline slowdown occurs due to memory access delays
Solution Approach 1:
The decision tree is segmented into multiple levels, with phantom nodes inserted at intermediate levels to create parallel processing paths. This segmentation allows the inference engine to process multiple decision levels simultaneously rather than sequentially, resolving the contradiction between maintaining structural fidelity and improving processing speed.
Solution Approach 2:
The patent introduces a new dimensional structure by adding phantom decision nodes that create additional processing layers. This transforms the traditional single-path sequential execution into a multi-dimensional parallel processing architecture, enabling simultaneous evaluation of multiple decision paths while preserving the original tree's logical structure.
2Productivity
If the decision tree is inflated by inserting phantom decision nodes, then parallel processing capability is improved, but the device complexity increases
Solution Approach 1:
Phantom decision nodes are essentially copies of standard decision node structures that are inserted into the tree. These copied nodes replicate the functional behavior of regular nodes but serve as structural placeholders to enable parallel processing, increasing throughput while managing complexity through structural repetition rather than functional complexity.
Solution Approach 2:
The patent modifies the structural parameters of the decision tree by changing the number of levels and nodes through inflation. By adjusting these parameters (adding phantom nodes at specific levels), the system achieves higher processing throughput while controlling complexity through systematic parameter modification rather than fundamental architectural changes.
3Measurement precision
If more parameters are transmitted to the inference accelerator for the inflated decision tree, then processing accuracy is maintained, but input/output overhead increases
Solution Approach 1:
The patent extracts and eliminates redundant parameter transmissions by recognizing that phantom nodes serve as structural placeholders rather than functional decision points requiring data input. This extraction of unnecessary I/O operations reduces transmission overhead while maintaining inference accuracy by preserving only the essential parameters needed for actual decision-making nodes.
Data Source
AI summary
Processing within a computing environment is facilitated by establishing an inflated decision tree from a source decision tree, where the establishing includes inserting one or more phantom decision nodes into the source decision tree to obtain the inflated decision tree. Decision node data and leaf node data are ascertained for the inflated decision tree and provided to an inference accelerator to facilitate accelerated processing of the inflated decision tree, and determining which leaf node of a plurality of leaf nodes of the inflated decision tree is selected.