Data-Dependent Neural Network Processing With Conditional Execution
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Solution Overview
Problem
Existing neural network systems inefficiently process large amounts of data, leading to high power consumption and computational complexity due to unnecessary data handling and sequential processing without considering data characteristics.
Innovation Solution
A dynamic data-dependent neural network processing system with a conditional execution control circuit that analyzes input data to determine optimal processing paths, modifying sequences, and selecting appropriate hardware accelerators for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional neural network systems process all input data through complete sequential processing, then comprehensive analysis is achieved, but power consumption and computational resources increase significantly
Solution Approach 1:
The system dynamically adjusts the processing sequence and termination points based on data characteristics. The conditional execution control circuit modifies the fixed sequential processing flow to adaptively skip or terminate processing for certain data patterns, enabling energy-efficient computation while maintaining necessary accuracy
Solution Approach 2:
The system changes processing parameters (such as processing depth, sequence order, and termination conditions) based on input data characteristics. By analyzing data features beforehand, the system adjusts processing parameters to minimize unnecessary computations and reduce power consumption
2Measurement precision
If neural network systems process large amounts of data through multiple hierarchical layers, then classification accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary analysis of input data characteristics before initiating full processing. The conditional execution control circuit evaluates data features in advance to determine optimal processing paths, preventing unnecessary processing of data that doesn't require full hierarchical analysis
Solution Approach 2:
The processing workflow is segmented into conditional branches based on data characteristics. Different data patterns are routed through different processing paths with appropriate depth and complexity, allowing accurate classification while minimizing processing time for each specific case
3Productivity
If hardware accelerators are added to increase processing speed, then computational efficiency improves, but hardware cost and system complexity increase
Solution Approach 1:
The conditional execution control circuit serves multiple functions: data analysis, processing sequence determination, and termination control. This single control mechanism manages both CPU and hardware accelerator operations, eliminating the need for separate control hardware and reducing overall system complexity
Solution Approach 2:
The system uses its own control circuit to intelligently determine when to invoke hardware accelerators based on data characteristics. The conditional execution control circuit autonomously manages hardware resource allocation without external intervention, optimizing processing speed while maintaining simple hardware architecture
Data Source
AI summary
Dynamic data-dependent neural network processing systems and methods increase computational efficiency in neural network processing by uniquely processing data based on the data itself and/or configuration parameters for processing the data. In embodiments, this is accomplished by receiving, at a controller, input data that is to be processed by a first device in a first layer of a sequence of processing layers of a neural network using a first set of parameters. The input data is analyzed to determine whether to modify it, whether processing the (modified) data in a second layer would conserve at least one computational resource, or whether to apply a different set of parameters. Depending on the determination, the sequence of processing layers is modified, and the (modified) data are processed according to the modified sequence to reduce data movements and transitions, thereby, conserving computational resources.