Hello Edge: Keyword Spotting on Microcontrollers

Zhang, Yundong; Suda, Naveen; Lai, Liangzhen; Chandra, Vikas

doi:10.48550/arxiv.1711.07128

Cited by 132 publications

(166 citation statements)

References 12 publications

Supporting

Mentioning

165

Contrasting

Order By: Relevance

“…Previous approaches relying on the conventional ADC + preprocessing flow such as Hello Edge [24] and EGRU, [31] even with an extremely optimized DNN model or hardware accelerator, will have an upper limit on the energy efficiency due to the data acquisition and pre-processing overhead. Our proposed system architecture with a binarizing AFE provides a method to overcome this fundamental limitation.…”

Section: Discussionmentioning

confidence: 99%

“…On their left, there are the systems that include the analog front end and binarization of weights ad activations. Here, the system achieves sub-mW consumption with 80% accuracy in the task, 4% less accuracy than the solution from HelloEdge [24], which consumes more than 10 mW.…”

Section: Accuracy-energy Trade-offmentioning

confidence: 98%

“…Unfortunately, activations are more sensitive to quantization in terms of accuracy drop. The approximation of the activations is quasi-lossless down to 8 bit [24], and it allows the hardware to parallelize four computations over a 32-bit register.…”

Section: A Size-optimized and Quantized Dnnsmentioning

confidence: 99%

“…Pioneers of edge computing for keyword spotting [24] manage to implement a keyword spotting model on a cortex M4 based MCU: they explore a different kind of models, and they reach the state of the art accuracy even in memoryconstrained devices. Moreover, they present an implementation with 8-bit quantization for weights and activations to use SIMD instruction and parallelize four operations in just one instruction.…”

Section: B Kws Applications and Implementationsmentioning

confidence: 99%

“…In the proposed system, the same microphone is connected to all these filters. The next step is to choose the right filter shape for further classification: log-Mel spectrogram and the following MFCC features are intensively used in sound-based classification systems [13], [24]. For this reason, we choose corner frequencies equally-spaced in the Mel domain within the selected range.…”

Section: A Analog Front-endmentioning

confidence: 99%

See 4 more Smart Citations

Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks

Cerutti¹,

Cavigelli²,

Andri³

et al. 2022

Preprint

View full text Add to dashboard Cite

Keyword spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings, either activating them through wake-word or directly as a human-computer interface. For many applications, KWS is the entry point for our interactions with the device and, thus, an always-on workload. Many smart devices are mobile and their battery lifetime is heavily impacted by continuously running services. KWS and similar always-on services are thus the focus when optimizing the overall power consumption.This work addresses KWS energy-efficiency on low-cost microcontroller units (MCUs). We combine analog binary feature extraction with binary neural networks. By replacing the digital preprocessing with the proposed analog front-end, we show that the energy required for data acquisition and preprocessing can be reduced by 29×, cutting its share from a dominating 85% to a mere 16% of the overall energy consumption for our reference KWS application.Experimental evaluations on the Speech Commands Dataset show that the proposed system outperforms state-of-the-art accuracy and energy efficiency, respectively, by 1% and 4.3× on a 10-class dataset while providing a compelling accuracyenergy trade-off including a 2% accuracy drop for a 71× energy reduction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Accuracy-energy Trade-offmentioning

confidence: 98%

Section: A Size-optimized and Quantized Dnnsmentioning

confidence: 99%

Section: B Kws Applications and Implementationsmentioning

confidence: 99%

Section: A Analog Front-endmentioning

confidence: 99%

See 3 more Smart Citations