Recently, interest in fractional‐order inductors (FOIs) has increased since they allow for accurate and robust models of dynamical systems to be designed. However, practical implementation of these models has not been possible due to the lack of single FOI realizations. To address this challenge, in this work, we propose a simple‐to‐realize fractional‐order inductor design with variable constant phase angle (CPA). The design relies on characteristics of transverse electromagnetic (TEM) mode propagating on a coaxial structure filled with conductive material, more specifically NaCl‐water solution and flour‐based mixtures. The CPA of the resulting FOI can be tuned by changing the conductivity of the dough mixture. Analysis of the proposed FOI design show that the CPA can vary in a range from 0° to 90°. Two of these CPA values are verified against experiments in the frequency band changing from 1 to 10 MHz.
The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques are applied to implement a neural network on limited hardware resources. Quantization is one of the most efficient network compression techniques allowing to reduce the memory footprint, latency, and energy consumption. This paper provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and links software-based quantization approaches to IMC hardware implementation. Moreover, open challenges, QNN design requirements, recommendations, and perspectives along with an IMC-based QNN hardware roadmap are provided.
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