Neural Network Weight Tying for Local Device Deployment
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Solution Overview
Problem
Deep neural networks (DNNs) are computationally expensive and memory-intensive, making it challenging to implement them on local devices due to limited resources, which hinders their use in applications beyond web/cloud-based systems.
Innovation Solution
The implementation of an artificial neural network training algorithm using weight tying, which reduces memory footprint by sharing weights and eliminating the need for multipliers through quantization and clustering techniques, allowing for efficient training and deployment on devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are implemented on local devices, then machine learning capabilities are improved, but memory usage and computational complexity increase
Solution Approach 1:
The patent segments the neural network weights into multiple groups and applies quantization to each group separately. This segmentation allows the network to maintain its learning capability while reducing the precision requirements for individual weight parameters, thereby reducing overall memory usage.
Solution Approach 2:
The patent changes the precision parameter of weight storage from high precision (32-bit floating point) to low precision (integer quantization). By transforming the parameter representation format, the network achieves comparable performance with significantly reduced memory footprint suitable for local devices.
2Adaptability or versatility
If deep neural networks are implemented on local devices, then machine learning capabilities are improved, but computational power requirements increase
Solution Approach 1:
The patent substitutes complex floating-point multiplication operations with simpler integer arithmetic operations. By replacing the mechanical computation system from floating-point to integer-based, the computational power requirement is reduced while maintaining the essential neural network functionality on resource-constrained devices.
Solution Approach 2:
The patent changes the computational parameter from high-precision floating-point arithmetic to low-precision integer arithmetic. This parameter change reduces the computational complexity and power requirements while preserving the network's ability to perform machine learning tasks locally.
3Quantity of substance
If weight quantization is applied to reduce memory footprint, then memory usage is reduced, but training complexity increases
Solution Approach 1:
The patent segments the weight quantization process into distinct phases: initial high-precision training, weight analysis and clustering, and final quantized training. This segmentation of the training process manages complexity by breaking down the challenging quantization task into more manageable stages.
Solution Approach 2:
The patent performs preliminary weight analysis and clustering before final quantized training. By preparing the weight distribution and determining optimal quantization parameters in advance, the actual training process becomes simpler and more efficient, reducing the perceived training complexity.
Data Source
AI summary
An apparatus comprising circuitry that implements an artificial neural network training algorithm that uses weight tying.