Neural Network Construction via Software Hox Genomes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex neural network architectures, while performing accurately and robustly, are expensive to operate in terms of power, computing resources, and memory, making them challenging to deploy on resource-constrained devices such as smartphones and laptops.
Innovation Solution
The use of software hox genomes to encode neural networks, allowing for the application of evolutionary algorithms to automatically construct neural networks with optimized architectures for specific tasks, thereby reducing complexity and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex neural network architectures are used, then performance accuracy and robustness are improved, but power consumption, computing resource requirements, and memory usage increase
Solution Approach 1:
The patent segments the neural network architecture into modular components (encoders, decoders, recurrent units) that can be independently selected and combined. This allows the system to achieve high performance through intelligent combination of simple modules rather than using complex monolithic architectures, thereby reducing overall computational complexity and power consumption while maintaining accuracy.
Solution Approach 2:
The patent implements dynamic architecture selection where the neural network structure adapts based on task requirements and available computational resources. The system can dynamically adjust the number and type of modules used in different operational contexts, enabling optimal performance-energy tradeoffs by deploying simpler architectures when resources are constrained while utilizing more complex structures when computational power is available.
2Reliability
If complex neural network architectures are used, then performance accuracy and robustness are improved, but computing resource requirements and memory usage increase
Solution Approach 1:
By dividing the neural network into discrete, reusable modules (encoders for feature extraction, decoders for output generation, recurrent units for temporal processing), the patent enables systematic construction of architectures that achieve high robustness through intelligent module composition rather than relying on complex pre-defined structures, thereby reducing computing resource requirements.
Solution Approach 2:
The patent creates a universal library of neural network modules that can be reused across different tasks and architecture configurations. This modularity allows a single set of computing resources to support diverse architectural needs, reducing the peak resource requirements by enabling task-specific architecture selection rather than requiring infrastructure designed for the most complex possible architecture.
3Reliability
If complex neural network architectures are used, then performance accuracy is improved, but ease of deployment on resource-constrained devices deteriorates
Solution Approach 1:
The patent segments the neural network into standardized modules that can be independently optimized and deployed. This modular approach simplifies deployment on resource-constrained devices by allowing selective deployment of only the necessary modules for each task, rather than deploying complete complex architectures, thereby improving ease of operation while maintaining accuracy.
Solution Approach 2:
The patent enables parameter changes in architecture configuration (such as number of modules, module types, repetition counts) without changing the fundamental deployment framework. This allows the same deployment infrastructure to support varying levels of complexity by adjusting parameters, making deployment on resource-constrained devices easier while maintaining the ability to achieve high performance when needed.
Data Source
AI summary
To construct a neural network for image processing or other tasks, a population of software hox genomes is generated, each software hox genome encoding a neural network and comprising, for each of a plurality of zones of a neural network, a hox gene and at least one norm gene, where the norm gene encodes the zone and the hox gene controls at least one of: a quantity of instances of the zone in the neural network, a position of the zone in the neural network, a gradient of weights in the zone, a depth of nested zones within the zone, an amount of bias to be applied to weights in the zone. An evolutionary algorithm is applied to the population of software hox genomes where a fitness function of the evolutionary algorithm comprises a performance of a neural network encoded by a software hox genome on the image processing or other task.