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<p>In mammalian hearts, the Sinoatrial (SA) node cells act as the fundamental pacemaker location. The intricate patterns of SA node activity can be elucidated by a series of differential equations featuring non-linear functions. This study presents a novel approach to improve the digital representation of the SA node cell model. The results consist of reduced hardware requirements, enhanced speed and accuracy, and lowered implementation costs. The proposed method involves converting the non-linear functions found in the original model into exponential functions represented as <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(2^x\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mn>2</mn> <mi>x</mi> </msup> </math></EquationSource> </InlineEquation>. This conversion gives rise to a set of mathematical equations that eliminate the need for multipliers. These equations are subsequently refined by minimizing the number of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(2^x\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mn>2</mn> <mi>x</mi> </msup> </math></EquationSource> </InlineEquation> terms, leading to a more effective emulation of the SA model’s biological characteristics. To validate the concept, the suggested model is successfully synthesized and implemented on an FPGA. The implementation outcomes demonstrate a substantial enhancement in operating frequency, showing a 3.4-fold increase compared to the original model. Moreover, there is a noticeable 47% reduction in power consumption. The reduced hardware requirements also enable running a significantly larger number of neurons, precisely 12 times more, on a single FPGA board compared to the original model.</p>

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Sinoatrial node cells implementation by low cost digital hardware

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Ali Naderi,
  • Gilda Ghanbarpour,
  • Milad Ghanbarpour,
  • Saeed Haghiri,
  • Arash Ahmadi

摘要

In mammalian hearts, the Sinoatrial (SA) node cells act as the fundamental pacemaker location. The intricate patterns of SA node activity can be elucidated by a series of differential equations featuring non-linear functions. This study presents a novel approach to improve the digital representation of the SA node cell model. The results consist of reduced hardware requirements, enhanced speed and accuracy, and lowered implementation costs. The proposed method involves converting the non-linear functions found in the original model into exponential functions represented as \(2^x\) 2 x . This conversion gives rise to a set of mathematical equations that eliminate the need for multipliers. These equations are subsequently refined by minimizing the number of \(2^x\) 2 x terms, leading to a more effective emulation of the SA model’s biological characteristics. To validate the concept, the suggested model is successfully synthesized and implemented on an FPGA. The implementation outcomes demonstrate a substantial enhancement in operating frequency, showing a 3.4-fold increase compared to the original model. Moreover, there is a noticeable 47% reduction in power consumption. The reduced hardware requirements also enable running a significantly larger number of neurons, precisely 12 times more, on a single FPGA board compared to the original model.