Dynamic Packet Compression With Per-Vector Quantization for AI Data Transfer
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Solution Overview
Problem
Existing AI training and inference processes face challenges in efficiently communicating data over lower bandwidth channels due to the limitations of traditional compression algorithms, which require significant computing resources and may impact the convergence of AI algorithms when precision is reduced.
Innovation Solution
Implementing per-vector scaled quantization and sparsity mechanisms to compress individual network packets dynamically, maintaining a master copy of full precision data, and applying compression piecemeal to reduce memory footprint and network traffic while minimizing precision loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression algorithms are used to reduce data size, then memory consumption and network traffic are reduced, but computing resources are significantly consumed and processing speed decreases
Solution Approach 1:
The patent extracts only the essential information from the data by identifying and transmitting only changed elements or significant differences between data versions, rather than compressing the entire data structure. This selective extraction reduces data size without requiring heavy compression algorithms that would slow down processing.
Solution Approach 2:
The patent changes the representation parameters of data by using different precision levels for different data elements, dynamically adjusting the amount of information stored or transmitted based on importance. This allows selective reduction of data size for less critical elements while maintaining high precision for important data, avoiding the need for uniform compression that would slow down processing.
2Quantity of substance
If quantization is applied to reduce precision, then data compression is achieved, but convergence of AI training algorithms is negatively impacted
Solution Approach 1:
The patent applies different precision levels to different data elements based on their importance and impact on model convergence. Critical parameters maintain high precision while less important ones use lower precision, allowing compression without significantly impacting AI training convergence.
Solution Approach 2:
The patent applies quantization selectively to only portions of the data that are less critical to model convergence, rather than uniformly quantizing all data. This partial application of compression maintains the convergence properties of AI training while still achieving some data size reduction.
3Measurement precision
If full precision data is transmitted over the network, then data accuracy is maintained, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts and transmits only the essential information needed to maintain data accuracy, such as differences from a reference version or selectively chosen significant digits. This extraction approach maintains sufficient data accuracy while dramatically reducing network traffic volume.
Solution Approach 2:
The patent transmits full precision data only for critical elements that require high accuracy, while using compressed representations for less critical data. This partial transmission of full precision data maintains overall data accuracy while reducing total network traffic.
Data Source
AI summary
Data networks that include an ingress port and multiple per-vector scaled quantization encoders are configured to respond to receiving a vector of values at the ingress port by operating the per-vector scaled quantization encoders on the vector to generate reduced vectors, score each of the reduced vectors according to a precision loss, and select one of the reduced vectors to inject into the network based on the score.