Tuning Machine Learning Models for Resource-Constrained Devices
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Solution Overview
Problem
Resource-constrained devices, such as smart appliances, face challenges in implementing machine learning models due to limited processing power, memory, and battery life, and delegating tasks to the cloud increases complexity and cost, with network bottlenecks being a performance hindrance.
Innovation Solution
The method involves evaluating and tuning machine learning model implementations to optimize resource usage by identifying candidate alterations in computer-readable instructions, using a lookup table to address constraints on memory and computational power, allowing the models to operate efficiently on resource-constrained devices without requiring significant hardware changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If deep neural networks are implemented on powerful computing systems in the cloud, then processing power and performance are improved, but device complexity and cost increase for resource-constrained devices
Solution Approach 1:
The patent extracts the machine learning model execution from the resource-constrained device and delegates it to cloud-based computing systems. The device only needs to handle data preprocessing and result display, while the computationally intensive neural network operations are performed remotely on powerful server infrastructure, thus achieving high processing power without increasing local device complexity
Solution Approach 2:
The patent introduces an intermediary cloud computing layer between the resource-constrained device and the machine learning model execution. This intermediary handles the computational burden, allowing the device to maintain simplicity while still accessing advanced AI capabilities through network communication
2Reliability
If machine learning models are executed locally on resource-constrained devices, then real-time performance and reliability are improved, but memory and computational power requirements increase
Solution Approach 1:
The patent segments the machine learning system into two parts: data preprocessing and model execution. The resource-constrained device handles the preprocessing segment locally, ensuring real-time response and reliability, while the memory-intensive model execution segment is delegated to cloud systems with sufficient memory resources
Solution Approach 2:
The patent moves the computational workload from the spatial dimension (local device memory) to the network dimension (cloud infrastructure). By utilizing remote computing resources accessible through network communication, the system achieves reliable real-time performance without consuming local device memory
3Power
If cloud-based machine learning execution is used, then processing capabilities are improved, but network dependency and performance bottlenecks increase
Solution Approach 1:
The patent performs preliminary data preprocessing and preparation on the resource-constrained device before transmitting data to the cloud. By preparing data locally in advance, the system reduces the amount of data that needs to be transmitted over the network and minimizes communication latency, thus mitigating network speed bottlenecks while still utilizing cloud processing power
Data Source
AI summary
The present disclosure is directed to methods and apparatus for tuning implementations of machine learning models for resource-constrained devices. In various embodiments, computer-readable instructions that utilize a trained machine learning model during execution of the computer-readable instructions on a resource-constrained device may be evaluated. Based on the evaluating, it may be determined that an amount of a first computing resource required by the resource-constrained device to execute the computer-readable instructions fails to satisfy a constraint related to the first computing resource. Based on the constraint, one or more candidate alterations to the computer-readable instructions may be identified.