43 research outputs found

    An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

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    Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE

    Vega: A Ten-Core SoC for IoT Endnodes with DNN Acceleration and Cognitive Wake-Up from MRAM-Based State-Retentive Sleep Mode

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    The Internet-of-Things (IoT) requires endnodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an IoT endnode system on chip (SoC) capable of scaling from a 1.7- μW fully retentive cognitive sleep mode up to 32.2-GOPS (at 49.4 mW) peak performance on NSAAs, including mobile deep neural network (DNN) inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile magnetoresistive random access memory (MRAM). To meet the performance and flexibility requirements of NSAAs, the SoC features ten RISC-V cores: one core for SoC and IO management and a nine-core cluster supporting multi-precision single instruction multiple data (SIMD) integer and floating-point (FP) computation. Vega achieves the state-of-the-art (SoA)-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3 TOPS/W for 8-bit DNN inference with hardware acceleration). On FP computation, it achieves the SoA-leading efficiency of 79 and 129 GFLOPS/W on 32- and 16-bit FP, respectively. Two programmable machine learning (ML) accelerators boost energy efficiency in cognitive sleep and active states

    A Noncontact ECG Sensing System With a Micropower, Ultrahigh Impedance Front-End, and BLE Connectivity

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    Capacitive noncontact electrodes offer a promising solution to improve comfort and unobtrusiveness in continuous monitoring ECG systems which are becoming more widespread in wearable devices. Most implementations based on off-the-shelf components require rather large electrodes and power budgets while custom application-specific integrated circuits (ASICs) face significantly higher adoption challenges. In this article, we present a novel micropower ultrahigh impedance front-end with an equivalent input capacitance of 70 fF in the ECG band, enabling contactless ECG acquisition with electrodes as small as 78.5 mm2 with a power consumption of only 15 μW15 \, \mu \text{W}. The front-end implements a novel power supply bootstrapping architecture and is integrated in a full acquisition system which includes a 24-bit analog to digital converter (ADC) and a bluetooth low energy (BLE)-capable Cortex M4 processor, achieving up to ten days of continuous operation on a 600 mAh PR44 battery

    A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime

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    In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance- The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario

    An 11.5% Frequency Tuning, -184 dBc/Hz Noise FOM 54 GHz VCO

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    This work presents a robust, low area, spectral pure 65 nm VCO for mm-wave applications. The varactor, an inversion mode MOS, takes advantage of the minimum feature provided by the technology to optimize capacitance tuning range and Q. The inductor is a 1 turn spiral. A combination of digital and analog tuning is chosen to lower VCO gain. Prototypes show the following measured results: 11.5% frequency tuning range around 54 GHz, phase noise at 10 MHz of -116 dBc/Hz and -122 dBc/Hz maximumand minimum in band, respectively. Power consumption is 7.2 mW

    A Wearable Device for Minimally-Invasive Behind-the-Ear EEG and Evoked Potentials

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    We present an unobtrusive and minimally invasive system for acquiring 8-channels of EEG behind the ear, to support brain activity monitoring in a socially acceptable fashion and outside clinical environments. Electrical performance are in line with those of clinical EEG systems (0.48 \u3bcV RMS integrated noise in the [0-100] Hz band, 106 dB CMRR) and setup is extremely fast, requiring only a mild cleaning of the skin before applying the device. The system can acquire typical brain activity rhythms and potentials evoked by sensory stimuli, which are at the basis of BCI systems. Current consumption is 6.2 rnA, plus 16 mA for Bluetooth\uae data transmission (4.5 hours life on a 100 mAh battery). The overall cost of components is below 50 USD
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