Machine Learning Model Compiler for Zero-Runtime Deployment
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Solution Overview
Problem
Existing machine learning frameworks face challenges in deploying models in hardware-limited environments due to memory and power constraints, making it difficult to embed models in devices with limited resources while maintaining accuracy.
Innovation Solution
A machine learning compiler generates self-contained executable models that eliminate the need for runtime environments, optimizing operations across layers by compiling machine learning models into standalone code, allowing execution in low-power, low-memory devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are deployed using traditional runtime environments, then model functionality is maintained, but memory footprint increases and device complexity increases
Solution Approach 1:
The patent extracts and eliminates the runtime environment from the deployment package. By using a compiler that generates standalone executable code directly in the target device's native language, the solution removes the need for separate runtime libraries and execution environments, thereby reducing memory footprint while maintaining model functionality.
Solution Approach 2:
The patent merges the model code with the execution code into a single integrated executable. The compiler combines the machine learning model operations with optimized native code in one unified package, eliminating the separation between model data and runtime execution environment, thus reducing overall memory requirements.
2Ease of operation
If machine learning models are deployed using runtime environments, then model operations can be executed, but device complexity increases
Solution Approach 1:
The patent extracts and removes the runtime environment layer entirely. By compiling models directly to native executable code, the solution eliminates the need for runtime interpreters, libraries, and execution frameworks, thereby simplifying the device architecture while preserving model execution capability.
Solution Approach 2:
The patent creates a direct copy of the model functionality in native code rather than using an interpreted or virtualized runtime environment. This direct translation approach maintains execution capability while avoiding the complexity overhead of runtime systems.
3Quantity of substance
If machine learning models are compiled to standalone executable code, then memory footprint is reduced, but compilation complexity increases
Solution Approach 1:
The patent introduces a compiler as an intermediary tool that bridges the high-level model representation and the low-level native executable code. This compiler handles the complex transformation process, abstracting the complexity from the deployment process while enabling efficient standalone execution with reduced memory footprint.
4Device complexity
If machine learning models are deployed in embedded devices, then hardware resource constraints are respected, but model accuracy may be compromised
Solution Approach 1:
The patent applies parameter changes through compiler optimizations that adapt the model execution to the target hardware's capabilities. By adjusting execution parameters, data types, and computation patterns during compilation, the solution optimizes the balance between hardware resource consumption and model accuracy for embedded devices.
Data Source
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AI summary
The subject technology provides a framework for executable machine learning models that are executable in a zero-runtime operating environment. This allows the machine learning models to be deployed in limited memory environments such as embedded domains. A machine learning compiler is provided to generate the executable machine learning models.