This article presents a 148-nW always-on wake-up system that drastically reduces the system power consumption of Internet of Things (IoT) sensor nodes while oftentimes operating in random-sparse-event (RSE) scenarios. To significantly reduce the long-term average (LTA) power consumption and realize multiapplication and intelligent event detection, three techniques are proposed: 1) In a three-stage pipelined event-driven architecture, a frame generator and a convolutional neural network intelligent inference engine (CNN IIE) in stage III are event-driven by the preliminary detectors in stage II, and the detectors are triggered by a level-crossing (LC) analog-to-digital converter (ADC), i.e., stage I, dramatically reducing the overall power consumption. 2) The clock-free pulse-based instant rate of change (IROC) feature extractor directly processes the asynchronous pulses of the LC-ADC outputs in the temporal domain instead of utilizing a conventional power-hungry frequency-domain method. 3) A reconfigurable IROC, the frame generator, and the CNN IIE provide adaptive intelligence for various IoT events, enhancing the accuracy of multipurpose detection with ultralow power. We demonstrate two artificial intelligence IoT (AIoT) applications at 0.6-V V _DD . For electrocardiogram (ECG) recognition, one example works at a typical event rate (ER) of ~4800/h, with an active power of 1.68 $\mu \text{W}$ and a precision of up to 99%; the other is used for keyword spotting (KWS), where the chip achieves 378 nW at ~720/h ER and 94% accuracy. The LTA power is bounded to 148 nW, while the event-driven chip is on call and waiting for events; this chip dominates the AIoT device battery life in RSE scenarios.

Automated specification energy compliance checking aims to check the compliance of building designs captured in building information models (BIMs) with energy requirements from contract specifications. However, automated extraction of requirements from specifications to support automated compliance checking is challenging because of the text complexities of the specifications, including hierarchically complex text structures, incomplete sentence structures, and a variety of levels of development (LODs). To address this challenge, this paper proposes a semantic information extraction method to extract building energy requirements automatically from specifications. Specifically, three new submethods are proposed to deal with these complexities: a domain-specific text splitting and stitching method, an incompleteness-aware sequential dependency extraction method, and a detail-aware LOD extraction method. The proposed information extraction method was tested in extracting thermal-insulation and lighting-power requirements from MasterFormat specifications. A performance of 96.8% recall and 97.6% precision was achieved on the testing data, which indicates the effectiveness of the proposed method.