Communication Autoencoder Training With Quantized Fixed-Point Networks
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Solution Overview
Problem
Existing communication systems face challenges in practical hardware implementation due to high memory requirements and computational complexity of neural networks, which are often not viable with available hardware resources, particularly in communication systems lacking graphic processing units (GPUs).
Innovation Solution
Implementing neural networks with compressed representations using quantization and fixed-point arithmetic, and employing evolutionary computation to train autoencoders for end-to-end communication systems, reducing the complexity and memory requirements while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are implemented with high precision floating point arithmetic for accurate communication system training, then measurement precision is improved, but device complexity and memory requirements increase significantly
Solution Approach 1:
The patent changes the numerical precision parameter from high-precision floating point arithmetic to low-precision fixed-point arithmetic. This parameter change allows the neural network to operate with reduced computational complexity and memory requirements while maintaining sufficient accuracy for communication system training, directly resolving the contradiction between precision and device complexity
Solution Approach 2:
The patent employs quantized fixed-point representations that can be implemented using simpler, more resource-efficient hardware compared to floating-point units. This approach uses computationally lighter operations that consume less memory and processing power, effectively replacing complex floating-point arithmetic with simpler fixed-point operations
2Measurement precision
If neural networks are implemented with high precision floating point arithmetic, then measurement precision is improved, but the use of energy increases due to GPU requirements
Solution Approach 1:
The patent changes the numerical precision parameter from high-precision floating point arithmetic to low-precision fixed-point arithmetic. This parameter change allows the neural network to operate with reduced computational complexity and memory requirements while maintaining sufficient accuracy for communication system training, directly resolving the contradiction between precision and device complexity
Solution Approach 2:
The patent employs quantized fixed-point representations that can be implemented using simpler, more resource-efficient hardware compared to floating-point units. This approach uses computationally lighter operations that consume less memory and processing power, effectively replacing complex floating-point operations
3Device complexity
If quantization is applied to reduce precision and computational complexity, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent changes the numerical precision parameter from high-precision floating point arithmetic to low-precision fixed-point arithmetic. This parameter change allows the neural network to operate with reduced computational complexity and memory requirements while maintaining sufficient accuracy for communication system training, directly resolving the contradiction between precision and device complexity
Data Source
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AI summary
An apparatus, method and computer program is described comprising:evaluating some or all of a current population of algorithms according to a metric, each algorithm of the population implementing a transmission system;selecting a subset of the algorithms of the current population based on the metric; generating an updated population of algorithms from said subset; and repeating the evaluating, selecting and generating, based on the updated population, until a first condition is reached.