Energy-Aware Library for Neural Network Frequency Scaling
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Solution Overview
Problem
Current computing systems face challenges in achieving energy and communication efficiency, particularly in deep learning applications, due to high power consumption and communication costs, which impact battery life and operational costs in devices and data centers.
Innovation Solution
An energy-aware communication library is developed for neural network applications, utilizing dynamic voltage-frequency scaling and sparse matrix representation to optimize frequency scaling and overlap communication and computation, reducing power usage and communication costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional computing techniques are used for deep learning applications, then processing capability is maintained, but energy consumption and communication costs increase
Solution Approach 1:
The patent implements dynamic voltage-frequency scaling that adjusts operating frequency based on workload characteristics and communication requirements. The system dynamically transitions between high-performance and energy-efficient modes, optimizing the balance between processing capability and energy consumption in real-time during deep learning operations
Solution Approach 2:
The system changes operational parameters including voltage and frequency levels to optimize energy efficiency. By adjusting these parameters based on computational intensity and communication patterns, the system reduces energy consumption while maintaining adequate processing capability for deep learning tasks
2Speed
If high frequency scaling is used to maintain performance, then processing speed is improved, but power consumption increases
Solution Approach 1:
The patent employs periodic performance monitoring and adaptive frequency adjustment. The system periodically evaluates computational workload and communication patterns, then adjusts frequency accordingly, creating a rhythm of high-performance bursts followed by energy-efficient operation that reduces overall power consumption while maintaining processing speed when needed
Solution Approach 2:
The system applies high frequency scaling only partially and selectively during periods of high computational demand, rather than maintaining maximum frequency continuously. This partial application of high performance mode reduces energy loss while ensuring processing speed is available when required by the deep learning workload
Data Source
AI summary
A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.