NDLI: Reinforcement learning based self-adaptive voltage-swing adjustment of 2.5D I/Os for many-core microprocessor and memory communication

Content Provider	IEEE Xplore Digital Library
Author	Huang Hantao Sai Manoj, P.D. Dongjun Xu Hao Yu Zhigang Hao
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Huang Hantao; Sai Manoj, P.D.; Dongjun Xu; Hao Yu) \|\| MediaTek Singapore Pte. Ltd., Singapore, Singapore (Zhigang Hao)
Abstract	A reinforcement learning based I/O management is developed for energy-efficient communication between many-core microprocessor and memory. Instead of transmitting data under a fixed large voltage-swing, an online reinforcement Q-learning algorithm is developed to perform a self-adaptive voltage-swing control of 2.5D through-silicon interposer (TSI) I/O circuits. Such a voltage-swing adjustment is formulated as a Markov decision process (MDP) problem solved by model-free reinforcement learning under constraints of both power budget and bit-error-rate (BER). Experimental results show that the adaptive 2.5D TSI I/Os designed in 65nm CMOS can achieve an average of 12.5mw I/O power, 4GHz bandwidth and 3.125pJ/bit energy efficiency for one channel under $10^{-6}$ BER, which has 18.89% power saving and 15.11% improvement of energy efficiency on average.
Starting Page	224
Ending Page	229
File Size	1540250
Page Count	6
File Format	PDF
ISBN	9781479962785
DOI	10.1109/ICCAD.2014.7001356
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bit error rate Receivers Voltage control Transmitters Tuning Noise Adaptation models
Content Type	Text
Resource Type	Article

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