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AI Sens., Volume 2, Issue 1 (March 2026) – 2 articles

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24 pages, 4131 KB  
Article
A Novel SRAM In-Memory Computing Accelerator Design Approach with R2R-Ladder for AI Sensors and Eddy Current Testing
by Kevin Becker, Martin Zimmerling, Matthias Landwehr, Dirk Koster, Hans-Georg Herrmann and Wolf-Joachim Fischer
AI Sens. 2026, 2(1), 2; https://doi.org/10.3390/aisens2010002 - 15 Jan 2026
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Abstract
This work presents a 6T-SRAM-based in-memory computing (IMC) system fabricated in a 180 nm CMOS technology. A total of 128 integrated polysilicon R2R-DACs for fully analog wordline control and performance analysis are integrated into the system. The proposed architecture enables analog computation directly [...] Read more.
This work presents a 6T-SRAM-based in-memory computing (IMC) system fabricated in a 180 nm CMOS technology. A total of 128 integrated polysilicon R2R-DACs for fully analog wordline control and performance analysis are integrated into the system. The proposed architecture enables analog computation directly inside the memory array and introduces a compact 1-bit per-column comparator scheme for energy-efficient classification without requiring ADCs. A dedicated pull-down-dominant SRAM sizing and an analog activation scheme ensure stable analog discharge behavior and precise control of the computation through time-dependent bitline dynamics. The system integrates a complete sensor front-end, which allows real eddy current data to be classified directly on-chip. Measurements demonstrate a performance density of 3.2 TOPS/mm2, a simulated energy efficiency of 45 TOPS/W at 50 MHz, and a measured efficiency of 3.4 TOPS/W at 5 MHz on silicon. The implemented online training mechanism further improves classification accuracy by adapting the SRAM cell states during operation. These results highlight the suitability of the presented IMC architecture for compact, low-power edge intelligence and sensor-driven machine learning applications. Full article
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38 pages, 11274 KB  
Review
A Review of Intelligent Self-Powered Sensing Systems Enabling Autonomous AIoT
by Hangrui Cui, Tianyi Tang and Huicong Liu
AI Sens. 2026, 2(1), 1; https://doi.org/10.3390/aisens2010001 - 22 Dec 2025
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Abstract
The rapid development of the Artificial Intelligence of Things (AIoT) has created unprecedented demands for distributed, long-term, and maintenance-free sensing systems. Conventional battery-powered sensors suffer from inherent drawbacks such as limited lifetime, high maintenance costs, and environmental concerns, which hinder large-scale deployment. Self-powered [...] Read more.
The rapid development of the Artificial Intelligence of Things (AIoT) has created unprecedented demands for distributed, long-term, and maintenance-free sensing systems. Conventional battery-powered sensors suffer from inherent drawbacks such as limited lifetime, high maintenance costs, and environmental concerns, which hinder large-scale deployment. Self-powered sensing technologies provide a transformative pathway by integrating energy harvesting and sensing into a single platform, thereby eliminating the reliance on external power supplies. This review systematically summarizes the key components of self-powered wireless sensing systems, with a particular focus on different energy harvesting technologies, self-powered sensing technologies, and the latest advances in low-power intelligent computation for diverse application scenarios. The integration of energy harvesting, self-sensing, and intelligent computation will make self-powered wireless sensing systems an inevitable direction for the evolution of AIoT, enabling sustainable, scalable, and intelligent monitoring networks. Full article
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