Control Neuron Population Modulates Base Neurons for Accuracy
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Solution Overview
Problem
Artificial neural networks face a trade-off between increasing inferencing accuracy and reducing computational footprint, where improving accuracy often requires increased computational resources and vice versa.
Innovation Solution
The implementation of a system with a control neuron population that modulates the activity of base neuron populations, allowing for increased inferencing accuracy at a fixed or reduced computational footprint by scaling operands internally produced by the base neurons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the computational footprint of the artificial neural network is increased, then the inferencing accuracy is improved, but the computational resources required increase
Solution Approach 1:
The neural network is segmented into two distinct populations: base neuron populations that perform the primary inferencing task and control neuron populations that modulate their activity. This segmentation allows the control neurons to efficiently regulate computational resources in the base neurons, achieving high accuracy without proportionally increasing overall computational footprint.
Solution Approach 2:
The control neuron populations dynamically modulate the activity of base neuron populations during both training and inferencing phases. This dynamic control mechanism allows the system to adapt computational resource allocation in real-time, maintaining high inferencing accuracy while optimizing computational footprint based on task requirements.
2Quantity of substance
If the computational footprint of the artificial neural network is decreased, then the computational resources required are reduced, but the inferencing accuracy deteriorates
Solution Approach 1:
The control neuron populations receive feedback signals during training and inferencing phases, enabling them to adjust the activity levels of base neuron populations accordingly. This feedback mechanism ensures that computational resources are allocated efficiently to maintain high inferencing accuracy even with a reduced computational footprint.
Solution Approach 2:
The control neurons modify operational parameters of base neurons through modulation during training and inferencing. By changing parameters such as activation thresholds and connection strengths dynamically, the system maintains high accuracy while operating with a smaller computational footprint.
Data Source
AI summary
Systems and techniques that facilitate neuronal activity modulation of artificial neural networks are provided. In various embodiments, an artificial neural network can comprise a set of base neuron populations that collectively generate, during an inferencing phase or a training phase of the artificial neural network, an inferencing task result based on a data candidate. In various aspects, the artificial neural network can comprise a control neuron population that is independent of the set of base neuron populations. In various instances, the control neuron population can modulate, during the inferencing phase or the training phase, neuronal activity of at least one base neuron population of the set of base neuron populations. In various cases, the control neuron population can modulate the neuronal activity of the at least one base neuron population by scaling one or more operands internally produced by the at least one base neuron population.