A Low-Power Analog Hardware Sigmoid-based Neural Network for Biomedical Applications
Vassilis Alimisis, Christos Dimas, Andreas Papathanasiou, Paul Peter Sotiriadis · 2025
This research presents a novel approach for implementing an artificial neural network using an analog hardware architecture. The core components of this architecture consist of current-mode circuits, which represent the class, and a voltage-mode comparator. All current-mode circuits are designed to operate with minimal bias current. For the voltage comparator, which handles the final decision-making process, a low-voltage amplifier is utilized. The operational principles of the architecture are thoroughly detailed and applied in a power-efficient configuration, operating at sub-microWatt levels with low power supply rails (0.6 V). The proposed design is validated on real-world biomedical classification tasks, achieving impressive classification accuracy exceeding 93%. The implementation is realized using a 90nm CMOS process and developed within the Cadence IC Suite for both schematic and layout design. To ensure the robustness of the proposed classifier, Monte Carlo analysis, covering both process variations and mismatches, as well as corner analysis, are conducted. A comparative analysis of the post-layout simulation results with an equivalent software-based classifier and relevant literature confirms the effective performance of the proposed architecture.