9.9 A Background-Noise and Process-Variation-Tolerant 109nW Acoustic Feature Extractor Based on Spike-Domain Divisive-Energy Normalization for an Always-On Keyword Spotting Device

Wang, Dewei; Kim, Sung Justin; Yang, Minhao; Lazar, Aurel A.; Seok, Mingoo

doi:10.1109/isscc42613.2021.9365969

Cited by 31 publications

(29 citation statements)

References 5 publications

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“…The transfer functions and filter parameters of type-I and type-II SSF filters are summarized in (27,28). Interestingly, both SSF types can be deployed as a BPF without requiring any external subtractor in contrast to the OTA-based (see Fig.…”

Section: Source-follower-based Filtersmentioning

confidence: 99%

“…The analog signal processing potentially has a higher power efficiency [21], [22], and thus it could be useful for tasks implemented on lowpower edge audio devices. The CT analog filters on the stateof-art edge audio ICs for VAD [23]- [25] and KWS [26], [27] adopt a set of second-order band-pass filters (BPFs). These circuits were derived from early generations of silicon cochlea designs starting from [1] and a summary of the various voltage-domain/current-domain CT filter designs can be found in [3] and Chapter 3 of [2].…”

Section: Introductionmentioning

confidence: 99%

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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: Source-follower-based Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kim¹,

Liu²

2022

Preprint

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“…Recent trends in keyword spotting may offer an interesting common task-level benchmark for neuromorphic designs and machine-learning accelerators in the near future. Indeed, the time dimension now becomes an essential component, and spiking auditory sensors can be used on standard datasets such as TIDIGITS or the Google Speech Command Dataset [283], [284]. For the promising use case of biosignal processing (see Section V-C), an EMG-and vision-based sensor fusion dataset for hand gesture classification was recently proposed in [285].…”

Section: B Open Challenges and Opportunitiesmentioning

confidence: 99%

Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial Intelligence

Frenkel¹,

Bol²,

Indiveri³

2021

Preprint

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While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of new alternative brain-inspired computing architectures that promise to achieve the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic intelligence represents a paradigm shift in computing based on the implementation of spiking neural network architectures tightly co-locating processing and memory. In this paper, we provide a comprehensive overview of the field, highlighting the different levels of granularity present in existing silicon implementations, comparing approaches that aim at replicating natural intelligence (bottom-up) versus those that aim at solving practical artificial intelligence applications (top-down), and assessing the benefits of the different circuit design styles used to achieve these goals. First, we present the analog, mixed-signal and digital circuit design styles, identifying the boundary between processing and memory through time multiplexing, in-memory computation and novel devices. Next, we highlight the key tradeoffs for each of the bottom-up and top-down approaches, survey their silicon implementations, and carry out detailed comparative analyses to extract design guidelines. Finally, we identify both necessary synergies and missing elements required to achieve a competitive advantage for neuromorphic edge computing over conventional machine-learning accelerators, and outline the key elements for a framework toward neuromorphic intelligence.

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“…State-of-the-art systems for always-on keyword spotting combine SNNs with techniques in analog feature extraction: for example, it is possible to approximate mel-frequency spectrograms with band-pass filters, clipping amplifiers, and half-wave rectifiers. In this context, a recent publication [44] has improved the robustness of keyword spotting to the presence of background noise by prototyping a nonlinear circuit which approximates per-channel energy normalization (PCEN) via an integrate-and-fire (IAF) scheme.…”

Section: Analog Signal Processing and Spiking Neural Networkmentioning

confidence: 99%

“…The new generation of mixed-signal (analog-digital) and neuromorphic architectures for pattern recognition pushes the energy efficiency of acoustic sensors to an extreme level. For example, a single AA battery (3.9 W h) would contain enough energy to power the SNN-based printed circuit board of [44] for centuries. At first glance, this calculation may seem to render the debate on energyharvesting sensors altogether moot.…”

Section: Analog Signal Processing and Spiking Neural Networkmentioning

confidence: 99%

Energy Efficiency is Not Enough:Towards a Batteryless Internet of Sounds

et al. 2021

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This position paper advocates for digital sobriety in the design and usage of wireless acoustic sensors. As of today, these devices all rely on batteries, which are either recharged by a human operator or via solar panels. Yet, batteries contain chemical pollutants and have a shorter lifespan than electronic components: as such, they hinder the autonomy and sustainability of the Internet of Sounds at large. Against this problem, our radical answer is to avoid the use of batteries altogether; and instead, to harvest ambient energy in real time and store it in a supercapacitor allowing a few minutes of operation. We show the inherent limitations of battery-dependent technologies for acoustic sensing. Then, we describe how a lowcost Micro-Controller Unit (MCU) could serve for audio acquisition and feature extraction on the edge. In particular, we stress the advantage of storing intermediate computations in ferroelectric random-access memory (FeRAM), which is nonvolatile, fast, endurant and consumes little. As a proof of concept, we present a simple-minded detector of sine tones in background noise, which relies on a fixed-point implementation of the fast Fourier transform (FFT). We outline future directions towards bioacoustic event detection and urban acoustic monitoring without batteries nor wires. CCS CONCEPTS• Hardware → Sound-based input / output; Digital signal processing; Power estimation and optimization; Impact on the environment; • Computer systems organization → Real-time systems; • Networks → Sensor networks.

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9.9 A Background-Noise and Process-Variation-Tolerant 109nW Acoustic Feature Extractor Based on Spike-Domain Divisive-Energy Normalization for an Always-On Keyword Spotting Device

Cited by 31 publications

References 5 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

Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial Intelligence

Energy Efficiency is Not Enough:Towards a Batteryless Internet of Sounds

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