Function approximation is crucial in Flexible Electronics (FE), where applications demand efficient computational techniques within strict constraints on size, power, and performance. Devices like wearables and compact sensors are constrained by their limited physical dimensions and energy capacity, making traditional digital function approximation challenging and hardware-demanding. This paper addresses function approximation in FE by proposing a systematic and generic approach using a combination of Analog Building Blocks (ABBs) that perform basic mathematical operations such as addition, multiplication, and squaring. These ABBs serve as the foundation for constructing splines, which are then employed in the creation of Kolmogorov-Arnold Networks (KANs), improving the approximation. The analog realization of KAN offers a promising alternative to digital solutions, providing significant hardware benefits, particularly in terms of area and power consumption. Our design achieves a 125x reduction in area and a 10.59% power saving compared to a digital spline with 8-bit precision. Results also show that the analog design introduces an approximation error of up to 7.58% due to both the design and parasitic elements. Nevertheless, KANs are shown to be a viable candidate for function approximation in FE, with potential for further optimization to address the challenges of error reduction and hardware cost.

翻译：函数逼近在柔性电子学中至关重要，该领域的应用要求在尺寸、功耗和性能的严格约束下实现高效的计算技术。可穿戴设备与紧凑型传感器等器件受限于其有限的物理尺寸与能量容量，使得传统的数字函数逼近方法面临挑战且对硬件要求较高。本文针对柔性电子学中的函数逼近问题，提出了一种系统化且通用的方法，该方法通过组合执行加法、乘法及平方等基本数学运算的模拟构建模块来实现。这些模拟构建模块构成了构建样条函数的基础，进而用于构建Kolmogorov-Arnold网络，从而提升逼近性能。KAN的模拟实现为数字解决方案提供了一种有前景的替代方案，尤其在面积与功耗方面带来了显著的硬件优势。与8位精度的数字样条相比，我们的设计实现了125倍的面积缩减与10.59%的功耗节省。结果同时表明，由于设计本身与寄生元件的影响，该模拟设计会引入最高7.58%的逼近误差。尽管如此，KAN被证明是柔性电子学中函数逼近的可行候选方案，并具备通过进一步优化以应对误差降低与硬件成本挑战的潜力。