Self-regulating Power Management for Neural Network Systems
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Solution Overview
Problem
Current power management systems for deep neural networks (DNNs) lack the ability to efficiently balance power consumption, processing speed, and accuracy, often requiring traditional computing's high power consumption for accurate performance, with limited user control over accuracy for specific applications.
Innovation Solution
A self-regulating power management system for DNNs that adjusts voltage and clock frequency based on error tolerance, using closed-loop feedback to maintain acceptable accuracy levels, allowing DNNs to operate within defined error bounds while optimizing power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional power management is used for DNNs, then processing accuracy is maintained, but power consumption is high
Solution Approach 1:
The system dynamically adjusts voltage and clock frequency based on real-time error rate monitoring. The power management is not static but adapts continuously to maintain acceptable accuracy while minimizing power consumption, allowing the DNN to operate at optimal power-accuracy tradeoff points
Solution Approach 2:
The system implements closed-loop feedback by monitoring error rates and using this information to adjust power settings. The error rate information feeds back to the power management logic, which then modifies voltage and frequency to maintain acceptable accuracy while reducing power consumption when possible
2Loss of energy
If power settings are reduced to save energy, then power efficiency improves, but error rate increases
Solution Approach 1:
The system changes operational parameters (voltage and clock frequency) to optimize the tradeoff between energy efficiency and accuracy. By adjusting these parameters based on monitored error rates, the system can operate at lower power settings when accuracy requirements are met, thereby improving energy efficiency without sacrificing necessary precision
3Adaptability or versatility
If user control over accuracy is increased, then application-specific performance improves, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring error rate thresholds and accuracy bounds before DNN operation. This allows application-specific accuracy requirements to be set in advance without adding complexity to the real-time operation, as the system simply compares measured error rates against pre-defined thresholds
Data Source
AI summary
A neural network runs a known input data set using an error free power setting and using an error prone power setting. The differences in the outputs of the neural network using the two different power settings determine a high level error rate associated with the output of the neural network using the error prone power setting. If the high level error rate is excessive, the error prone power setting is adjusted to reduce errors by changing voltage and/or clock frequency utilized by the neural network system. If the high level error rate is within bounds, the error prone power setting can remain allowing the neural network to operate with an acceptable error tolerance and improved efficiency. The error tolerance can be specified by the neural network application.