9.7 A 184 µ W Real-Time Hand-Gesture Recognition System with Hybrid Tiny Classifiers for Smart Wearable Devices

Lu, Yuncheng; Le, Van Loi; Kim, Tony Tae-Hyoung

doi:10.1109/isscc42613.2021.9365963

Cited by 10 publications

(12 citation statements)

References 3 publications

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“…Finally, Table 1 compares the proposed gesture sensor with conventional sensors [4][5][6][7]. The power dissipations of the photo sensor-based [5] and capacitive sensor-based [6] techniques are large because they are active type sensors; that is, they require the light by LED and electric field, and their generation consumes significant power.…”

Section: Measurement Resultsmentioning

confidence: 99%

“…These wearable gesture sensors can recognize various complicated hand gestures, whereas the users must put the sensor devices on their hand, which is less convenient. Therefore, various remote gesture sensing techniques without any wearable devices have been proposed [4][5][6][7]. The gesture sensing technique based on an image sensor (camera) [4] is one of the most reliable methods for recognizing hand gestures.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, various remote gesture sensing techniques without any wearable devices have been proposed [4][5][6][7]. The gesture sensing technique based on an image sensor (camera) [4] is one of the most reliable methods for recognizing hand gestures. It can offer gesture recognition over a long distance and detect complicated gestures [4].…”

Section: Introductionmentioning

confidence: 99%

“…The gesture sensing technique based on an image sensor (camera) [4] is one of the most reliable methods for recognizing hand gestures. It can offer gesture recognition over a long distance and detect complicated gestures [4]. However, image sensors and image processing consume large amounts of power, and bulky components such as lenses are required.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the power, a gesture sensor using infrared rays was proposed in [7]. Although the power dissipation of this sensor is smaller compared to those of the abovementioned sensors [4][5][6], it is still large because complicated signal processing is required to recognize gestures from infrared rays emitted from the human hand. Therefore, further power reduction is required to implement gesture sensors in battery-operated devices.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-Low Power Hand Gesture Sensor Using Electrostatic Induction

Fuketa

2021

Sensors

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This paper presents an ultra-low power hand gesture sensor using electrostatic induction for mobile devices. Two electrodes, which consist of electret foils stacked on metal sheets, are used to recognize two gestures such as hand movements from left to right and right to left. The hand gesture recognition is realized by detecting the electrostatic induction currents induced by hand movements. However, the electrostatic induction currents are significantly small; hence, a hand gesture recognition chip is first designed in this study to amplify and detect the small electrostatic induction currents with low power. This chip is fabricated in a commercial 180 nm complementary metal oxide semiconductor (CMOS) process, and the measurement results indicate that the fabricated gesture recognition chip consumes 406 nW, which is less than 1/100th of the power dissipation of conventional gesture sensors.

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Section: Measurement Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ultra-Low Power Hand Gesture Sensor Using Electrostatic Induction

Fuketa

2021

Sensors

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Trending IC design directions in 2022

Chan¹,

Cheng²,

Deng³

et al. 2022

J. Semicond.

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For the non-stop demands for a better and smarter society, the number of electronic devices keeps increasing exponentially; and the computation power, communication data rate, smart sensing capability and intelligence are always not enough. Hardware supports software, while the integrated circuit (IC) is the core of hardware. In this long review paper, we summarize and discuss recent trending IC design directions and challenges, and try to give the readers big/cool pictures on each selected small/hot topics. We divide the trends into the following six categories, namely, 1) machine learning and artificial intelligence (AI) chips, 2) communication ICs, 3) data converters, 4) power converters, 5) imagers and range sensors, 6) emerging directions. Hope you find this paper useful for your future research and works.

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Pianissimo: A Sub-mW Class DNN Accelerator With Progressively Adjustable Bit-Precision

Suzuki,

Yu,

Yasunaga

et al. 2024

IEEE Access

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With the widespread adoption of edge AI, the diversity of application requirements and fluctuating computational demands present significant challenges. Conventional accelerators suffer from increased memory footprints due to the need for multiple models to adapt to these varied requirements over time. In such dynamic edge conditions, it is crucial to accommodate these changing computational needs within strict memory and power constraints while maintaining the flexibility to support a wide range of applications. In response to these challenges, this article proposes a sub-mW class inference accelerator called Pianissimo that achieves competitive power efficiency while flexibly adapting to changing edge environment conditions at the architecture level. The heart of the design concept is a novel datapath architecture with a progressive bit-by-bit datapath. This unique datapath is augmented by software-hardware (SW-HW) cooperative control with a reduced instruction set computer processor and HW counters. The integrated SW-HW control enables adaptive inference schemes of adaptive/mixed precision and Block Skip, optimizing the balance between computational efficiency and accuracy. The 40 nm chip, with 1104 KB memory, dissipates 793-1032 µW at 0.7 V on MobileNetV1, achieving 0.49-1.25 TOPS/W at this ultra-low power range.INDEX TERMS Ultra-low power, sub-mW, progressive bit-serial datapath, bit-scalable accelerators, adaptive inference, neural network, SW-HW cooperative control.

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9.7 A 184 µ W Real-Time Hand-Gesture Recognition System with Hybrid Tiny Classifiers for Smart Wearable Devices

Cited by 10 publications

References 3 publications

Ultra-Low Power Hand Gesture Sensor Using Electrostatic Induction

Ultra-Low Power Hand Gesture Sensor Using Electrostatic Induction

Trending IC design directions in 2022

Pianissimo: A Sub-mW Class DNN Accelerator With Progressively Adjustable Bit-Precision

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