A research team from National Yang Ming Chiao Tung University (NYCU) has developed a technology that can significantly improve heat dissipation in multicore processors.
Laptops, mobile phones and computer servers commonly have multicore processors, the university said in a statement.
As the number of cores increases, the challenges of connectivity within the network-on-chip (NoC) also rise, it said.
Photo courtesy of National Yang Ming Chiao Tung University
As the core clock speed increases, so do temperatures, which affect operating performance and reliability, it said.
The research team of members of the NYCU Cerebral and Reliable System on Chip Laboratory led by NYCU electrical engineering associate professor Chen Kun-chi (陳坤志) proposed a more cost-effective mechanism that can accurately predict temperatures in NoCs and apply dynamic temperature management via adaptive reinforcement learning technology, greatly improving the performance of a system’s temperature management, the university said.
The paper, “Adaptive Machine Learning-Based Proactive Thermal Management for NoC Systems,” was published last year in the international journal IEEE Transactions on Very Large Scale Integration Systems, the university said.
The team won the journal’s Best Paper Award, the first Taiwanese to receive the honor in the past 30 years, it said.
The machine-learning temperature management technology adopts least-mean-square adaptive filtering theory optimization, which improves temperature prediction accuracy as a processor undertakes different tasks, Chen said.
It uses an adaptive reinforcement learning method to adjust the throttling ratio via real-time feedback from temperature, predicted temperature and system throughput, with the goal to achieve optimal thermal management while maximizing performance, he said.
The research showed that, compared with traditional methods, the adaptive learning method significantly reduces temperature prediction errors and improves system performance, he said.
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