Pudukotai Dinakarrao, S. M., Yu, H., Huang, H., & Xu, D. (2016). A Q-Learning Based Self-Adaptive I/O Communication for 2.5D Integrated Many-Core Microprocessor and Memory. IEEE Transactions on Computers, 65(4), 1185–1196. https://doi.org/10.1109/tc.2015.2439255
Software; Theoretical Computer Science; Hardware and Architecture; Computational Theory and Mathematics; multiprocessing systems; CMOS; bit error rate; BER; Q-learning; error statistics; 2.5D integrated many-core microprocessor; 2.5D memory; I/O management; communication power; energy-efficient I/O communi
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Abstract:
A self-adaptive output-voltage swing adjustment is introduced in the design of energy-efficient I/O communication for 2.5D integrated many-core microprocessor and memory. Instead of transmitting signal with large voltage swing, a Q-learning based I/O management is deployed to adaptively adjust the I/O output-voltage swing under constraints of both communication power and bit error rate (BER). Simulation results show that the proposed adaptive 2.5D I/Os (in 65 nm CMOS) can achieve an average of 12.5 mW I/O power, 4 GHz bandwidth and 3.125 pJ/bit energy efficiency for one channel under 10^{-6} BER. With the use of conventional Q-learning and further accelerated Q-learning, we can achieve 12.95 and 18.89 percent power reduction and 14 and 15.11 percent energy efficiency improvement when compared to the use of uniform output-voltage swing based I/O communication.
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Research Areas:
Automation and Robotics: 30% Computer Engineering and Software-Intensive Systems: 30% Logic and Computation: 40%
