Always-On Sparse Event Wake-Up Detectors: A Review

Gazivoda, Marko; Bilas, Vedran

doi:10.1109/jsen.2022.3162319

Cited by 6 publications

(5 citation statements)

References 70 publications

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“…Acoustic wake-up microsystems have the characteristics of low power consumption, small size, and long battery life, which lead to large-scale and long-term acoustic monitoring. The wake-up technology significantly improves energy efficiency and battery life, especially for the detection of rare events [ 3 ]. In this paper, the applications the acoustic wake-up microsystems can be used are summarized.…”

Section: Applicationsmentioning

confidence: 99%

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Acoustic Wake-Up Technology for Microsystems: A Review

Yang

Zhao

2023

Micromachines

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Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.

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Section: Applicationsmentioning

confidence: 99%

“…For many applications in unattended scenarios, events of concern rarely occur. Continuous detection of such sparse events wastes most power of the microsystem [ 3 ]. Thus, a wake-up strategy for microsystems is studied.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Wake-Up Technology for Microsystems: A Review

Yang

Zhao

2023

Micromachines

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“…This allows to move from a near-sensor to a hybrid near-sensor in-sensor ML application. The approach is closely related to the use of wake-up detectors [8]. However, it differs from most wake-up detectors in that the data is not solely analyzed to wake up more complex hardware.…”

Section: From Near-to In-sensor Motion Classificationmentioning

confidence: 99%

Making Machine Learning More Energy Efficient by Bringing It Closer to the Sensor

Brehler,

Camphausen,

Heidebroek

et al. 2023

IEEE Micro

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Processing data close to the sensor on a low-cost, low power embedded device has the potential to unlock new areas for machine learning (ML). Whether it is possible to deploy such ML applications or not depends on the energy efficiency of the solution. One way to realize a lower energy consumption is to bring the application as close as possible to the sensor. We demonstrate the concept of transforming an ML application running near to the sensor into a hybrid near-sensor in-sensor application. This approach aims to reduce the overall energy consumption and we showcase it using a motion classification example, which can be considered as a simpler sub-problem of activity recognition. The reduction of energy consumption is achieved by combining a convolutional neural network with a decision tree. Both applications are compared in terms of accuracy and energy consumption, illustrating the benefits of the hybrid approach.

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“…[ 16 ] However, significant continuous signal processing is still necessary to determine whether a particular event has taken place, even if the event occurs only rarely—a problem known as sparse event detection. [ 17 ] In traditional sensing paradigms, information is transferred to a central location, where the measured quantities are analyzed. This results in continuous bandwidth and power consumption, and potential privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

In‐Sensor Passive Speech Classification with Phononic Metamaterials

Dubček,

Moreno‐Garcia,

Haag

et al. 2024

Adv Funct Materials

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Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always‐on computers. However, their use in machine learning applications has not been explored due to the complexity of their design process. Current phononic metamaterials are restricted to simple geometries (e.g., periodic and tapered) and hence do not possess sufficient expressivity to encode machine learning tasks. A non‐periodic phononic metamaterial, directly from data samples, that can distinguish between pairs of spoken words in the presence of a simple readout nonlinearity is designed and fabricated, hence demonstrating that phononic metamaterials are a viable avenue towards zero‐power smart devices.

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Always-On Sparse Event Wake-Up Detectors: A Review

Cited by 6 publications

References 70 publications

Acoustic Wake-Up Technology for Microsystems: A Review

Acoustic Wake-Up Technology for Microsystems: A Review

Making Machine Learning More Energy Efficient by Bringing It Closer to the Sensor

In‐Sensor Passive Speech Classification with Phononic Metamaterials

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