Neural Network Data Transmission Congestion Control
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Solution Overview
Problem
Neuromorphic systems, particularly spiking neural networks, face challenges in data transmission due to delays and jitters in communication links, which introduce noise and inaccuracies in signal decoding, and existing communication networks fail to optimally support the unique requirements of spike-based data transmission.
Innovation Solution
A method and apparatus for congestion level control in neural networks that involve sending temporally encoded data sequences with adaptive encoding configurations based on feedback from receiving nodes to mitigate errors and reduce data transmission rates during congestion, using mechanisms like inhibition windows, rate transcoding, and rate limiting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If temporal encoding is used to transmit spike-based data in neural networks, then information representation efficiency is improved, but transmission accuracy deteriorates due to delays and jitters in communication links
Solution Approach 1:
The system dynamically adapts the encoding configuration based on network conditions. The transmitting node modifies encoding parameters such as time bin width and encoding scheme in response to feedback from the receiving node about observed errors, allowing the system to optimize between information efficiency and transmission accuracy under varying congestion levels
Solution Approach 2:
A feedback mechanism is implemented where the receiving node monitors decoding errors and transmits this information back to the transmitting node. This feedback loop enables the transmitting node to adjust its encoding configuration to mitigate errors caused by communication delays and jitters, directly addressing the reliability issue while maintaining temporal encoding benefits
2Productivity
If data transmission rate is increased to improve productivity, then communication efficiency is improved, but transmission errors increase due to congestion in the communication link
Solution Approach 1:
The system dynamically adjusts the data transmission rate and encoding configuration based on observed error rates and network congestion conditions. When errors are detected, the transmitting node adapts by modifying encoding parameters or reducing transmission rate, allowing the system to navigate between productivity and reliability trade-offs
Solution Approach 2:
The transmitting node changes encoding parameters such as time bin width, encoding scheme type, and transmission rate based on feedback from the receiving node. These parameter adjustments allow the system to reduce transmission errors during congestion while maintaining efficient communication when conditions are favorable
Data Source
AI summary
A method, performed by a transmitting node of a neural network, is provided for congestion level control. The method includes: sending, to a receiving node of the neural network, a plurality of sequences of data, the sequences of data being temporally encoded according to an encoding configuration; sending, to the receiving node, an indication of the encoding configuration, enabling the sequences of data to be decoded; receiving, from the receiving node, feedback including an indication of an error experienced in decoding the sequences of data; and adapting the encoding configuration based on the feedback.