Integrated Circuit Fine-Tuning for Noisy Memory ML Models
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Solution Overview
Problem
Large machine learning models face performance degradation due to noise sources in high-density memory, requiring substantial computational resources for training and deployment, especially when transitioning from external systems to less resourceful computing architectures.
Innovation Solution
A computing architecture that stores machine learning models in dense but noisy memory is fine-tuned to counteract noise sources, using a combination of a model core and a fine-tuning portion, with the fine-tuning performed on the architecture to maintain performance without extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-density memory is used to store machine learning model parameters, then memory capacity is improved, but noise sources cause performance degradation
Solution Approach 1:
The patent converts the harmful noise from high-density memory into a beneficial calibration signal. By intentionally introducing controlled noise and measuring its effect on model performance, the system identifies offset values that compensate for the noise. This transforms the previously harmful interference into a useful source of information for optimizing memory operation parameters.
Solution Approach 2:
The patent implements a feedback loop where model performance is continuously monitored when using high-density memory. The measured performance degradation feeds back into the calibration process, which adjusts the offset values stored in lookup tables. This closed-loop system continuously optimizes the compensation parameters based on actual performance data, ensuring sustained model accuracy despite memory noise.
2Reliability
If model fine-tuning is performed to counteract memory noise, then model performance is improved, but computational resources are required
Solution Approach 1:
The patent performs calibration actions in advance by pre-computing offset values and storing them in lookup tables during manufacturing or initialization. This preliminary calibration eliminates the need for continuous real-time fine-tuning during model inference, significantly reducing computational resource requirements while maintaining performance. The heavy computational work is done once beforehand, not repeatedly during operation.
Solution Approach 2:
The patent uses lightweight, easily computable offset values stored in compact lookup tables instead of expensive, resource-intensive fine-tuning operations. These simple calibration parameters act as disposable corrections that can be quickly applied without requiring substantial computational power, replacing the need for complex iterative optimization during runtime.
3Device complexity
If large machine learning models are deployed on modest systems, then resource requirements are reduced, but performance degradation occurs due to noise
Solution Approach 1:
The patent changes the operational parameters of the memory system by introducing offset values that compensate for noise effects. By modifying the memory read values through these offsets stored in lookup tables, the system maintains accurate model parameter retrieval even on modest hardware. This parameter adjustment allows large models to run on resource-constrained devices without performance loss.
Data Source
AI summary
Methods and systems that involve computing architectures with large machine learning models stored in dense memories are disclosed herein. A disclosed method includes adding a machine learning model to a computing architecture, where the machine learning model was trained on the computing architecture after being added to the computing architecture, and the machine learning model is stored in at least one memory on the computing architecture. The disclosed method also includes fine-tuning the machine learning model to counteract a decrease in performance of the machine learning model on the computing architecture that is attributable to the at least one memory. The fine-tuning may include training the machine learning model on the computing architecture.