Function Approximation Using Analog Building Blocks in Flexible Electronics
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
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.
