On-Device ML Model Testing via Input Channel Evaluation
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Solution Overview
Problem
On-device neural network models used in connected devices face challenges in robust and frictionless testing, especially when integrated deeply into hardware or communication stacks, making external observation and testing difficult and resource-intensive.
Innovation Solution
A remote testing apparatus and method for on-device machine learning models, which includes a training input generator, a test unit, and a model evaluator. This system designs training input information to produce expected outputs, applies these inputs to the device, and compares the obtained outputs with the expected outputs to evaluate the model, all without requiring software access to the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If on-device model training is implemented to allow models to adapt to usage context, then performance benefits are achieved, but the structure of the deployed neural network changes significantly over time in an unpredictable way
Solution Approach 1:
The patent applies preliminary action by performing model testing before deployment and periodically after deployment. The testing framework evaluates model structures in advance to predict potential changes from on-device training, allowing operators to prepare appropriate responses. This preliminary testing approach helps maintain reliability by identifying structural changes before they affect production performance.
Solution Approach 2:
The patent implements feedback mechanisms where model outputs are continuously monitored and fed back to the testing system. The testing framework compares actual model behavior against expected behavior, and when deviations are detected, the system triggers retesting or model updates. This closed-loop feedback ensures that performance benefits from adaptation are maintained while detecting unwanted structural changes.
2Adaptability or versatility
If neural network models are deeply integrated in hardware or communication stack, then functionality is improved, but testing and verification by external observers becomes problematic
Solution Approach 1:
The patent introduces an intermediary testing framework that operates between the external observer and the deeply integrated model. This framework uses standardized input datasets and output evaluation metrics that can interface with hardware-integrated models through defined protocols. The intermediary layer translates external testing requirements into forms compatible with hardware-integrated models, enabling verification without direct access to internal model structures.
Solution Approach 2:
The patent creates a universal testing apparatus that can evaluate multiple types of models (software-based, hardware-integrated, communication-stack embedded) through a single standardized interface. The testing framework is designed to be agnostic to the specific integration method, using universal input/output evaluation that works across different deployment scenarios, thus maintaining testing capability despite varied integration approaches.
3Reliability
If periodic testing is implemented to ensure standards compliance, then reliability is maintained, but resources are wasted for widely deployed models if it requires devices to run additional functions dedicated only to self-testing
Solution Approach 1:
The patent merges the testing function with the model's normal inference operations. The testing framework reuses existing model computation pathways and hardware resources, combining testing with regular model execution. By merging these functions, the system achieves periodic testing for reliability without requiring separate dedicated testing hardware or significant additional energy consumption, as the same computational resources serve both testing and production purposes.
Solution Approach 2:
The patent implements self-service testing where the model tests itself using its own computational resources and infrastructure. The framework leverages the model's inherent processing capabilities to perform self-evaluation without external testing equipment. This self-service approach minimizes additional energy consumption by utilizing already-allocationed resources, allowing widely deployed models to maintain compliance without proportional increases in device energy usage.
Data Source
AI summary
The invention proposes systems and methods for testing on-device machine learning models such as network models of a device based on an access to normal input and output channels, wherein a structure of a model is designed through training such that the model produces distinctive outputs to a given set of test inputs only so long as its internal structure remains in a desired state. Models may be rendered susceptible to such testing via model pre-training with training inputs designed to train the model into an appropriate structure and/or to cause the model to produce a distinctive output if later presented with a certain set of test inputs. If the structure of the model remains within allowable limits, the device will produce a predictable output when tested. If not, the device may be enforced to return to a pre-trained model.