IEEE Open Journal of Circuits and Systems (Jan 2022)

Design and Implementation of an On-Demand Maximum-Likelihood Sequence Estimation (MLSE)

  • Mohammad Emami Meybodi,
  • Hector Gomez,
  • Yu-Chun Lu,
  • Hossein Shakiba,
  • Ali Sheikholeslami

DOI
https://doi.org/10.1109/OJCAS.2022.3173686
Journal volume & issue
Vol. 3
pp. 97 – 108

Abstract

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This paper proposes a novel design for Maximum Likelihood Sequence Estimation (MLSE) used in ultra-high-speed wireline communication. We take advantage of the propagated errors caused by Decision-Feedback Equalizer (DFE) to activate and guide the MLSE, thereby reducing its complexity. The design is customized for a 4-PAM, 1 + D signaling system, and synthesized in 16nm FinFET TSMC Technology. For comparison purposes, a conventional MLSE is also synthesized in the same technology. The synthesis report confirms that the proposed design consumes 1/10 of the power and occupies 1/15 of the area required by the conventional MLSE while having a comparable bit error rate.

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