We report an always-on event-driven asynchronous wake-up circuit with trainable pattern recognition capabilities to duty-cycle power-constrained Internet-of-Things (IoT) sensor nodes. The wake-up circuit is based on a level-crossing analog-to-digital converter (LC-ADC) employed as a feature-extraction block with automatic activity-sampling rate scaling behavior. A novel asynchronous digital logic classifier for sequential pattern recognition is presented. It is driven by the LC-ADC activity and trained to minimize classification errors due to falsely detected events. As proof-of-concept, a prototype of the wake-up circuit is fabricated in 130nm CMOS technology within 0.054 mm2 of active area, covering up to 2.6 kHz of input signal bandwidth. The prototype has been first validated by interfacing it with a commercial accelerometer to classify hand gestures in real-time, reaching 81% of accuracy with only 2.2 μW at 1-V supply. To highlight the flexibility of the design, a second application, detecting pathologic electrocardiogram beats is also discussed.

A 2.2-μ W Cognitive Always-On Wake-Up Circuit for Event-Driven Duty-Cycling of IoT Sensor Nodes

Benini, Luca
2018

Abstract

We report an always-on event-driven asynchronous wake-up circuit with trainable pattern recognition capabilities to duty-cycle power-constrained Internet-of-Things (IoT) sensor nodes. The wake-up circuit is based on a level-crossing analog-to-digital converter (LC-ADC) employed as a feature-extraction block with automatic activity-sampling rate scaling behavior. A novel asynchronous digital logic classifier for sequential pattern recognition is presented. It is driven by the LC-ADC activity and trained to minimize classification errors due to falsely detected events. As proof-of-concept, a prototype of the wake-up circuit is fabricated in 130nm CMOS technology within 0.054 mm2 of active area, covering up to 2.6 kHz of input signal bandwidth. The prototype has been first validated by interfacing it with a commercial accelerometer to classify hand gestures in real-time, reaching 81% of accuracy with only 2.2 μW at 1-V supply. To highlight the flexibility of the design, a second application, detecting pathologic electrocardiogram beats is also discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/673294
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