Neural Network Resolution Adaptation for Computational Resource Constraints
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Solution Overview
Problem
Artificial neural networks face challenges in real-time operations due to computational resource limitations, particularly in modeling complex neurological functions like the retina, which can exhaust available resources without optimizations for performance improvement.
Innovation Solution
Implement dynamic resource allocation techniques by reducing the resolution of functions performed by processing units in artificial neural networks based on available computational resources and compensating for this reduction by adjusting network weights, such as reducing retina resolution and increasing corresponding weights, allowing for optimized trade-offs between computational speed and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full resolution is maintained in neural network processing, then measurement precision is improved, but productivity deteriorates due to computational resource exhaustion
Solution Approach 1:
The patent applies dynamics by making the processing resolution adjustable rather than fixed. The system dynamically changes the resolution of neural network processing based on available computational resources, allowing it to adapt between high precision and high speed modes. This is achieved through configurable processing units that can operate at different resolution levels.
Solution Approach 2:
The patent changes the resolution parameter of the processing units to resolve the contradiction. By adjusting the resolution parameter down when computational resources are limited and up when resources are abundant, the system maintains productivity while preserving measurement precision when possible. Weight compensation further adjusts parameters to maintain performance across different resolution settings.
2Measurement precision
If computational resources are allocated to maintain high processing resolution, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The system dynamically adjusts resolution based on energy availability. When energy resources are constrained, the processing units operate at lower resolutions, reducing energy consumption. When energy is abundant, the system can afford higher resolution processing. This dynamic adaptation resolves the contradiction between precision and energy use.
Solution Approach 2:
The resolution parameter is changed based on energy constraints. The system monitors available energy and adjusts the processing resolution parameter accordingly, lowering it when energy is scarce to reduce computational energy consumption while maintaining acceptable precision levels.
3Productivity
If resolution is reduced to improve computational speed, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent uses weight compensation as a counterweight to offset the precision loss from resolution reduction. When resolution is lowered to improve speed, the system adjusts the weights of neural network connections to compensate for the reduced detail, maintaining measurement precision despite the lower resolution input.
Solution Approach 2:
The system changes multiple parameters simultaneously - reducing resolution parameter to improve speed while adjusting weight parameters to compensate and maintain precision. This coordinated parameter adjustment resolves the contradiction between productivity and measurement precision.
4Measurement precision
If device complexity is increased to maintain processing quality, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically managing its own resource allocation and resolution settings. The neural network processing units autonomously adjust their operation based on available computational resources without requiring complex external management, improving ease of operation while maintaining processing quality.
Solution Approach 2:
The processing units are designed to be universal, capable of operating at multiple resolution levels and adapting to different resource conditions. This multi-functionality eliminates the need for separate systems for different precision requirements, simplifying operation while maintaining the ability to deliver high measurement precision when needed.
Data Source
AI summary
Methods and apparatus are provided for processing in an artificial nervous system. According to certain aspects, resolution of one or more functions performed by processing units of a neuron model may be reduced, based at least in part on availability of computational resources or a power target or budget. The reduction in resolution may be compensated for by adjusting one or more network weights.