Automated ML Model Tuning for Edge Deployment
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Solution Overview
Problem
The deployment of machine learning models on edge devices is hindered by the need for manual tuning to match the hardware capabilities of each device, which is time-consuming and costly due to differences in hardware architecture and resource constraints.
Innovation Solution
An apparatus and method that determine an initial model performance, modify the model based on user requirements such as accuracy, latency, and energy consumption, and iteratively optimize the model until it meets these requirements through operations like quantization, model size modification, and concurrent execution, using a compiler module and tuner module that can work with various accelerators without manual optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tuning is performed to match hardware capabilities of each device, then model deployment compatibility is improved, but time consumption and cost increase
Solution Approach 1:
The system performs automatic model tuning where the compilation module and tuner module autonomously optimize the machine learning model for the target device's hardware capabilities without requiring manual developer intervention. The tuner module automatically determines tuning operations based on device characteristics, eliminating the need for developers to manually tune models for each hardware platform.
Solution Approach 2:
The system performs preliminary model compilation and tuning operations before deployment to the target device. The compilation module pre-processes the model by determining compilation flows and generating optimized model code tailored to specific hardware architectures, so that when the model is deployed, it is already optimized and ready for efficient execution without requiring on-site manual tuning.
2Manufacturing precision
If manual optimization is performed for each hardware accelerator, then model performance is improved, but developer complexity increases
Solution Approach 1:
The compilation module acts as an intermediary between the generic machine learning model and the specific hardware accelerator. It translates the model into hardware-specific optimized code by determining appropriate compilation flows based on device characteristics, shielding developers from hardware-specific optimization details while achieving hardware-optimized performance.
Solution Approach 2:
The system provides a universal compilation and tuning framework that can automatically adapt machine learning models to multiple different hardware accelerators and device types. The tuner module and compilation module work together to generate device-specific optimizations through automatic tuning operations, allowing a single model to be deployed efficiently across diverse hardware platforms without requiring separate manual optimization for each device.
3Reliability
If model is modified to meet user requirements, then model performance satisfaction is improved, but number of operations increases
Solution Approach 1:
The system implements an iterative feedback loop where the tuner module determines tuning operations, applies them to modify the model, then evaluates whether the modified model meets user requirements. Based on the evaluation results, the system automatically determines additional tuning operations if needed, continuing this cycle until performance requirements are satisfied. This automated feedback mechanism ensures performance satisfaction while reducing the need for manual trial-and-error operations.
Data Source
AI summary
An apparatus, method and computer program is described comprising: determining an initial performance of a first model, wherein determining the initial performance comprises deploying the first model at a first device; determining one or more operations for modifying the first model based on at least the initial performance of the first model and one or more user requirements; modifying the first model by performing the one or more operations; determining whether a performance of the modified first model satisfies the one or more user requirements, wherein the determining comprises deploying the modified first model at the first device; and in the event that the modified first model does not satisfy the one or more user requirements, further modifying the first model by performing one or more further operations until the performance of the modified first model satisfies the one or more user requirements, wherein the determining further one or more operations based on at least the performance of the modified first model and the one or more user requirements.