AI Model Weight Compression During Training for Offline Runtime
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Solution Overview
Problem
Devices lacking sufficient storage, runtime memory, and compute resources are unable to run AI/ML models due to their resource-intensive requirements, necessitating remote access which introduces delays and costs, and conventional post-training compression degrades model quality.
Innovation Solution
Compress AI/ML models during training using palettes and coding procedures to iteratively adjust weight representation, allowing for dynamic adjustments based on quality impact, resulting in a compressed model that maintains high confidence and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If models are hosted on remote servers with adequate resources, then model functionality is provided, but network dependency increases and offline access is lost
Solution Approach 1:
The patent applies parameter changes by transforming model weights from high-precision floating-point format to low-precision discrete formats (1-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, 8-bit) during the training process. This parameter transformation enables the model to be deployed on resource-constrained devices while maintaining acceptable functionality, thereby allowing offline execution without network dependency.
2Quantity of substance
If model weights are compressed using conventional post-training methods, then storage requirements are reduced, but model quality and accuracy degrade
Solution Approach 1:
The patent implements preliminary action by performing weight compression and quantization during the training process itself, rather than as a post-training step. The system iteratively adjusts compression parameters and retrain the model to compensate for quantization errors, ensuring that model accuracy is maintained while achieving significant storage reduction. This preliminary compression action prevents the quality degradation that occurs with conventional post-training compression methods.
Solution Approach 2:
The patent employs feedback mechanisms by monitoring model performance metrics during the compression and retraining iterations. The system uses this feedback to adjust compression parameters and retrain the model, creating a closed-loop process that maintains model accuracy while achieving compression. The feedback ensures that quality degradation is minimized through iterative optimization.
3Manufacturing precision
If full-precision model weights are used, then model accuracy is maintained, but storage and runtime memory requirements increase
Solution Approach 1:
The patent transforms the parameter representation of model weights from high-precision floating-point format to low-precision discrete formats during training. By changing the parameter representation to 1-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, or 8-bit formats, the system achieves dramatic storage and memory reduction while maintaining model functionality through iterative retraining that compensates for the reduced precision.
4Ease of operation
If devices connect to remote servers over network, then model functionality is accessed, but latency and network costs increase
Solution Approach 1:
The patent enables model deployment on resource-constrained devices by transforming weight parameters to low-precision formats during training. This parameter transformation allows the model to run locally on devices with limited storage and compute resources, eliminating network dependency and the associated latency and costs while maintaining model functionality.
Data Source
AI summary
Techniques are provided for compressing weights of models during training of the models. A model is trained for execution on a target device. As part of training, weights of the model are compressed utilizing palettes to represent weight values using bits. A coding procedure, such as Huffman coding, is used to remove or modify the bit representations of infrequently utilized palettes. The model may be iteratively trained to compress the weights of the model in order to reduce the amount of storage consumed by the model without unduly sacrificing quality of the model. Reducing the size of the model provides the ability to deploy the model on devices that would otherwise lack storage and compute resources for storing and running an uncompressed version of the model.