An Acoustic Signal Processing Chip With 142-nW Voice Activity Detection Using Mixer-Based Sequential Frequency Scanning and Neural Network Classification

Oh, Sechang; Cho, Minchang; Shi, Zhan; Lim, Jongyup; Kim, Yejoong; Jeong, Seokhyeon; Chen, Yu; Rothe, Rohit; Blaauw, David; Hun-Seok, Kim; Sylvester, Dennis

doi:10.1109/jssc.2019.2936756

Cited by 38 publications

(28 citation statements)

References 17 publications

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“…This is because the center frequency of the BPF is controlled by the frequency of an external clock, rather than g m of the transconductors, therefore ω 0 can be precisely controlled over process, voltage, and temperature (PVT) variations. A chopper-based mixer with a sequentially varying clock frequency and a subsequent LPF stage was used in [59] where its operational principle is similar to that of lock-in amplifiers, also commonly used in bio-impedance sensors [52], [70]. This architecture achieved a 60 nW ultra-low-power consumption, however, because it sequentially demodulates over the desired frequency band, it cannot perform the filtering operation over its entire frequency range at once.…”

Section: Discussionmentioning

confidence: 99%

Continuous-Time Analog Filters for Audio Edge Intelligence: Review and Analysis on Design Techniques

Kim¹,

Liu²

2022

Preprint

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Silicon cochlea designs capture the functionality of the biological cochlea. Their use has been explored for cochlea prosthesis applications and more recently in edge audio devices which are required to support always-on operation. As their stringent power constraints pose several design challenges, IC designers are forced to look for solutions that use low standby power. One promising bio-inspired approach is to combine the continuous-time analog filter channels of the silicon cochlea with a small memory footprint deep neural network that is trained on edge tasks such as keyword spotting, thereby allowing all blocks to be embedded in an IC. This paper reviews the analog filter circuits used as feature extractors for current edge audio devices, starting with the original biquad filter circuits proposed for the silicon cochlea. Our analysis starts from the interpretation of a basic biquad filter as a two-integrator-loop topology and reviews the progression in the design of second-order low-pass and band-pass filters ranging from OTA-based to source-followerbased architectures. We also derive and analyze the small-signal transfer function and discuss performance aspects of these filters. The analysis of these different filter configurations can be applied to other application domains such as biomedical devices which employ a front-end bandpass filter.

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Section: Discussionmentioning

confidence: 99%

Continuous-Time Analog Filters for Audio Edge Intelligence: Review and Analysis on Design Techniques

Kim¹,

Liu²

2022

Preprint

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“…According to the state-ofthe-art ASICs for KWS and SV, the MFCC feature extraction occupies almost 40 % (~8 μW) of the total power consumption [6]. Furthermore, deep neural networks as recognition model prove useful for significant performance improvement [8]. However, massive computations and parameters are the bottlenecks of its low power implementation.…”

Section: Introductionmentioning

confidence: 99%

“…However, massive computations and parameters are the bottlenecks of its low power implementation. Although the power consumption of [7], [8] is ultra-low, they complete VAD and KWS containing only two classes.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Binarized Neural Network Using Analog-Intensive Feature Extraction for Keyword and Speaker Verification Wakeup

Zhao

Abubakar

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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An analog-intensive voice feature extraction method based on time-domain filtering is proposed in this paper, which solves the computational complexity problem in Mel Frequency Cepstral Coefficient (MFCC) extraction. Compared with MFCCs, the estimated power consumption of the proposed feature extraction is reduced by approximately 4×. Besides, a binarized neural network model called DSXNOR-Net with low parameters is proposed together with the generalized end-toend (GE2E) loss function for speaker verification. The feature extraction method and DSXNOR-Net are verified by Python.The proposed architecture achieves an optimal recognition accuracy of 98.3% on an English spoken digits corpus for keyword spotting (KWS) and 94.4% on TIMIT corpus for speaker verification (SV). The total estimated power consumption is reduced by approximately 2× and 4× for SV and KWS compared to the state-of-the-art results.

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“…It saves the total energy consumption or the average power consumption by reducing power in sleep mode for a long time; during that period the system does not need to be fully operated. The second solution is to develop low-power circuits both for active and sleep modes [18][19][20][21][22][23][24][25]. Lastly, the system includes an energy harvester to recharge the connected battery using environment energy [17].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting

Gao

Shao

et al. 2021

JLPEA

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Wireless sensor systems powered by batteries are widely used in a variety of applications. For applications with space limitation, their size was reduced, limiting battery energy capacity and memory storage size. A multi-exit neural network enables to overcome these limitations by filtering out data without objects of interest, thereby avoiding computing the entire neural network. This paper proposes to implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy constraint. The multi-exit design saves energy by 42.7% compared with the single-exit condition. A simulation result, based on an exemplary natural outdoor light profile and measured energy consumption of the proposed system, shows that the system can sustain its operation with a 3.2 kJ (275 mAh @ 3.2 V) battery by scarifying the accuracy only by 2.7%.

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An Acoustic Signal Processing Chip With 142-nW Voice Activity Detection Using Mixer-Based Sequential Frequency Scanning and Neural Network Classification

Cited by 38 publications

References 17 publications

Continuous-Time Analog Filters for Audio Edge Intelligence: Review and Analysis on Design Techniques

Continuous-Time Analog Filters for Audio Edge Intelligence: Review and Analysis on Design Techniques

An Energy-Efficient Binarized Neural Network Using Analog-Intensive Feature Extraction for Keyword and Speaker Verification Wakeup

Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting

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