A Framework for Designing and Analyzing Margin Propagation-Based Analog Correlators

Zhili Xiao, Albert Kilgore, Gert Cauwenberghs, Arun Natarajan, Aravind Nagulu, Shantanu Chakrabartty

Research output: Contribution to journalArticlepeer-review

Abstract

Precise estimation of correlation or similarity between two random variables lies at the heart of signal detection, target localization and pattern recognition. In this paper, we show that there exists a large class of multiplier-less analog correlators that can demonstrate a higher signal-to-noise ratio (SNR) compared to a conventional multiply-accumulate (MAC) based correlator. The multiplier-less design uses a Margin Propagation (MP) principle combining rectifying diodes in a symmetric circuit architecture. Using Price’s theorem we present a novel analytical framework that can be used to understand the steady-state behavioral response of different MP correlator circuits. The analytical results have been verified using transient and steady-state circuit simulations of correlator circuits designed in a standard CMOS process.

Keywords

  • analog correlators
  • margin-propagation
  • neural networks
  • Price’s theorem
  • Statistical correlation

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