Dynamic Inference Operator Optimization for Machine Learning Accelerators
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Solution Overview
Problem
Machine learning accelerators face inefficiencies in processing neural networks due to the need for large memory and bandwidth, leading to high hardware demands and power consumption, especially when handling large quantities of data for operations like activation values and weight values.
Innovation Solution
Dynamic inference operator optimization is implemented, where a code generator parses model metadata to determine input/output sizes and operator types, generating highly optimized arithmetic operators specific to the model, reducing processor cycles and latency without compromising accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning accelerators use large memory and bandwidth to handle large quantities of data for neural network operations, then processing capability is improved, but hardware demands and power consumption increase
Solution Approach 1:
The patent segments the neural network processing into distinct operator types (e.g., convolution, fully connected, pooling) and generates specialized code for each operator type. This segmentation allows the system to optimize processing for each specific operation rather than using a generic processing approach, improving processing capability while reducing overall power consumption through targeted optimizations.
Solution Approach 2:
The patent dynamically changes processing parameters at runtime based on the specific model being executed. The code generator analyzes model metadata to determine optimal processing parameters for each operator, such as input/output sizes and data types. This parameter adaptation enables efficient processing with reduced memory bandwidth requirements, thereby lowering power consumption while maintaining high processing capability.
2Productivity
If dynamic inference operator optimization is implemented to reduce processor cycles and latency, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary analysis of model metadata during the code generation phase, before runtime execution. The code generator extracts and analyzes model characteristics (input sizes, operator types, data types) in advance and generates optimized code accordingly. This preliminary action eliminates the need for complex runtime analysis and decision-making, reducing system complexity while maintaining high processing efficiency.
Solution Approach 2:
The patent introduces a code generator as an intermediary component between the model definition and the execution engine. This intermediary translates high-level model metadata into optimized low-level code, abstracting away the complexity of optimization algorithms from the runtime system. The code generator handles the complex analysis and optimization tasks, while the runtime system simply executes the pre-optimized code, thereby reducing overall system complexity.
Data Source
AI summary
Devices and techniques are generally described for machine learning hardware optimization. In some examples, a first computing device may receive first data describing a first machine learning model. A first operator type and a first input size for a first layer of the first machine learning model may be determined from the first data. First executable code may be generated that defines a first operator for the first layer of the first machine learning model. The first operator may be specific to the first input size and the first operator type. The first executable code may be stored in non-transitory computer-readable memory. In some examples, second data may be input into the first machine learning model. The first machine learning model may process the second data to generate first output data based at least in part on execution of the first code.