Ultra-low Power Microcontroller Research Articles

Dual action electrochemical bandage operated by a programmable multimodal wearable potentiostat

We have developed electrochemical bandage (e-bandage) prototypes that generate the reactive oxygen species hypochlorous acid (HOCl) or hydrogen peroxide (H2O2) for potential use to treat biofilm-infected wounds in humans. We have shown that both e-bandage-generated HOCl and H2O2 kill biofilms in vitro and in infected wounds on mice, with the former being more active in vitro. The H2O2-generating e-bandage, more so than the HOCl-generating e-bandage, was associated with improved healing of infected wounds. Here, a strategy in which H2O2 and HOCl are alternately generated—for dual action—was explored. The goal was to develop a programmable multimodal wearable potentiostat [PMWP] that can generate HOCl or H2O2, as needed. An ultralow-power microcontroller unit was developed to manage operation of the PMWP. The system was operated with a 260-mAh capacity coin battery and weighed 4.6 g, making it suitable for future small animal experiments (and ultimately, potential evaluation in humans). As assessed using electrochemical parameters, the device functioned comparably to a commercial benchtop potentiostat. To confirm antimicrobial activity, PMWP-controlled e-bandages were tested in vitro against clinical isolates of methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, Enterococcus faecium, and Candida auris. When programmed to deliver HOCl followed by H2O2, PMWP-controlled e-bandages exhibited activity against biofilms of all study isolates tested. Finally, we demonstrated the PMWP's usability in a murine wound infection model.

SEMG-driven Hand Dynamics Estimation with Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller.

Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.

An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves.

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted.

A Modular Architecture for Energy Efficient Wireless Sensor Networks Nodes

The current evolution of Wireless Sensor Networks (WSN) has led us towards real world problems such as the power supply of network nodes in an efficient and sustained way. The actual implementation of these networks calls for perpetually powered devices, since the constant changing of the batteries is not a realistic scenario. That is the main motivation behind the proposal of this new architecture, having the low power consumption as a key objective and an intelligent interaction with power supply modules. The energy efficiency of this platform comes from three different factors: the use of a last generation ultra low-power microcontroller, the use of different communication modes and the intelligent interaction with a harvesting module able to perpetually supply energy to the network nodes. Hence, the modularity of this platform, allowing for independent development of its components, is a very important characteristic. We present and discuss the major features of the proposed architecture, such as the used processor, how the modularity is achieved and how we intend to implement the energy solutions.

An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities.

Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However, achieving low latency and high classification performance remains challenging due to the inherent variability of electroencephalographic (EEG) signals across sessions and the limited onboard resources. This work proposes a comprehensive BMI workflow based on a CNN-based Continual Learning (CL) framework, allowing the system to adapt to inter-session changes. The workflow is deployed on a wearable, parallel ultra-low power BMI platform (BioGAP). Our results based on two in-house datasets, Dataset A and Dataset B, show that the CL workflow improves average accuracy by up to 30.36% and 10.17%, respectively. Furthermore, when implementing the continual learning on a Parallel Ultra-Low Power (PULP) microcontroller (GAP9), it achieves an energy consumption as low as 0.45mJ per inference and an adaptation time of only 21.5 ms, yielding around 25 h of battery life with a small 100 mAh, 3.7 V battery on BioGAP. Our setup, coupled with the compact CNN model and on-device CL capabilities, meets users' needs for improved privacy, reduced latency, and enhanced inter-session performance, offering good promise for smart embedded real-world BMIs.

A graphical derivation and statistical evaluation of simplified polynomials to determine vapour pressure deficit for use in ultra-low power microcontroller applications

The aim of this work has been to derive and statistically evaluate the accuracy of second-order and third-order polynomials to determine vapour pressure deficit (VPD). These polynomials take air temperature and relative humidity measurements to determine VPD without the use of an exponential function, as proposed by F W Murray in 1967. Replacing the exponential function with a 2nd or 3rd order polynomial may be beneficial in ultra-low power microcontroller-based measurement applications where; code size, memory usage and power requirements are critical design drivers. However, oversimplification may impact precision. This work presents alternative 2nd order and 3rd order equations that have been derived from a Murray equation dataset where VPD isothermal datasets were plotted against relative humidity. These linear relationships allow y = mx + c analysis where, (i) ‘c’ can be set to zero with a offset in the relative humidity data, and, (ii) ‘m’ can be derived from a 2nd of 3rd order polynomial where ‘m’ = f(T) and is derived using Excel-based fitting of the gradients from the isothermal datasets. The resulting ‘m’ = f(T) 2nd and 3rd order polynomials presented R 2 values of 0.998 04 and 0.999 98 respectively. A Bland-Altman statistical assessment was performed, where the Murray equation (reference) dataset is plotted against the difference between the reference and polynomial datasets using the same air temperature and relative humidity inputs. The difference datasets presented 2-sigma (95% confidence interval) variances for the 2nd and 3rd order polynomials as <±0.1 kPa and <±0.01 kPa respectively. The 2nd and 3rd order polynomials also resulted in a bias values of <0.0037 kPa and <0.0013 kPa respectively. These results suggest that a 3rd order polynomial equation could be used to determine VPD in ultra-low-power microcontroller measurement applications, with minimal impact on VPD measurement precision.

Semi-Passive RFID Electronic Devices With On-Chip Sensor Fusion Capabilities for Motion Capture and Biomechanical Analysis

This paper presents the development and testing of a novel electronic device for Wireless Motion Capture (W-MoCap), a technology that allows the reconstruction of movements of objects and body parts with many potential applications in various contexts. The device integrates UHF RFID technology with sensors for low-power backscattering communication. It consists of a Battery Assisted Passive UHF RFID chip, an Inertial Measurement Unit, an ultra-low power microcontroller, and a custom-designed edge-fed body-tolerant antenna operating at 866 MHz. The proper matching between the RFID chip and antenna is ensured through a well-designed L-match unbalanced network, and the separation of RF and DC signals is achieved with a meandered microstrip quarter-wavelength transformer, choke inductor, and decoupling capacitor. The designed and realized RFID W-MoCap Sensor-Tag has been thoroughly evaluated in terms of current consumption, front-end sensitivity, and sensing accuracy. Finally, five prototypes have been applied to specific segments of a human subject and successfully tested in a practical scenario for real time reconstruction of human movements.

UFBBR: A Unified Frequency and Back-Bias Regulation Unit for Ultralow-Power Microcontrollers in 28-nm FDSOI

Sensitivity to process, voltage, and temperature (PVT) variations constitutes a serious obstacle in ultralow-voltage/ultralow-power (ULV/ULP) circuits and systems. To address this challenge, we propose a unified frequency/back-bias regulation (UFBBR) macro embedded in a custom ULP ARM Cortex-M4 microcontroller unit (MCU) manufactured in 28-nm FDSOI technology. The UFBBR technique combines the generation of a 32-to-80 MHz system clock and asymmetric adaptive back biasing for PVT compensation. Relying on a novel dual-output frequency-locked loop, it senses both the logic speed and the N/PMOS process imbalance using back-bias-controlled oscillators, and generates adequate forward back-bias voltages with digitally-controlled oscillators followed by switched-capacitor charge-pumps for fast current actuation. Compared to a situation with zero back biasing and appropriate frequency/voltage margins, the UFBBR provides 15 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> of frequency boosting at 0.4 V or 180 mV of voltage reduction at 64 MHz. It leverages software-programmable configuration knobs to achieve a fast wake-up of 8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\upmu$</tex-math> </inline-formula> s and an in-lock power of 22 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\upmu$</tex-math> </inline-formula> W, with an area overhead below 0.032 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In sleep, it drives the back-bias voltages towards 0 V and disables the clock for minimum power consumption. These features help the MCU system achieve a minimum energy point of 5.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\upmu$</tex-math> </inline-formula> W/MHz and a sleep power of 7.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\upmu$</tex-math> </inline-formula> W.

A Sub-mW Cortex-M4 Microcontroller Design for IoT Software-Defined Radios

We present an Internet-of-Things (IoT) software-defined radio platform based on an ultra low-power microcontroller. Whereas conventional wireless IoT radios often implement a single protocol, we demonstrate that general-purpose microcontrollers running software implementations of wireless physical layers are a promising solution to increase interoperability of IoT devices. Yet, since IoT devices are often energy-constrained, the underlying challenge is to implement the digital signal processing of the radio in software while maintaining an overall very low power consumption. To overcome this problem, we propose an ultra low-power microcontroller architecture with an ARM Cortex-M4 processor for the protocol-specific computations and a hardware digital front-end for the generic signal processing. The proposed architecture has been prototyped in 28nm FDSOI and the physical layers of the well-known LoRa and Sigfox protocols have been implemented in software. Thanks to the efficient hardware/software partitioning and an ultra-low power digital implementation, experimental evaluations of the microcontroller prototype show sub-mW power consumptions (32 – 332 μW) for the digital signal processing of the software-defined radios.

Shelf control in retail stores via ultra-low and low power microcontrollers

Shelf control in retail stores via ultra-low and low power microcontrollers

Wide-Range Light Harvesting Module for Autonomous Sensor Nodes

A large number of autonomous devices is nowadays supported by renewable and green energy sources. A vital sub-circuit in such systems is the power converter circuit, which should efficiently transform and store the available energy. In order to obtain the maximum efficiency under varying energy conditions, various maximum power point tracking (MPPT) methods are used. In this work a complete harvesting module with battery management and MPPT is presented, suitable for a plethora of autonomous applications. A novel, low-complexity and ultra-low power consumption design is proposed, which offers very wide operating voltage and power range with high MPPT efficiency and very low power consumption. It can be combined with different harvesters, such as thermoelectric generators or photovoltaic panels and is able to work under widely varying energy conditions. As supported by experimental results, the proposed module covers a very wide working input power range, from 40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> up to 4 W, as well as a very wide input voltage range, from 650 mV up to 2.8 V with 96.5% average MPPT efficiency and a total power consumption of 3.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> at 3.6 V. The module relies on an embedded ultra-low power microcontroller unit (MCU) to perform the power management and MPPT operations, which can also be used for extra tasks (e.g., sensor reading). Using the proposed module, an autonomous sensor node was built, able to acquire acceleration measurements, and wirelessly communicate with a remote user in order to send an alert or stream the acquired sensor data in real time.

Gathering Energy of the Stray Currents in Electrified Railways Environment for Power Supply

The paper presents a new, unconventional energy harvesting (EH) method for supplying low-power devices on electrified railway lines that utilises stray currents and the non-zero potential of the rails to the ground. The EH device gathers the energy and stores it in batteries. It could even work in extremely unfavourable weather conditions and could be easily placed in almost any location. The presented real-life data show that the average available power is less than 250 mW and the average useful power is about 100 mW. This is enough to supply ultra-low power microcontrollers, which only occasionally use energy-consuming modules to perform measurements or communicate. The disadvantage of the EH method is the introduction of resistance between the rail and the earth, which increases stray currents and could increase the electrochemical corrosion of the rail. To reduce the impact of this resistance, a method for balancing the flowing charge is proposed. After balancing, the average of the flowing current is zero and electrochemical corrosion should be reduced. The proposed charge balancing algorithms could reduce the unbalanced charge to nearly zero at the expense of energy gathering efficiency, which decreases by 20–40%.

Design of UHF RFID Sensor-Tags for the Biomechanical Analysis of Human Body Movements

This paper presents the design and the development of a Battery Assisted Passive Radio-Frequency IDentification (RFID) tag in the Ultra High-Frequency band integrated with inertial measurement unit (IMU) sensors tested for the biomechanical analysis of human body movements. Enhanced by a compact and efficient meandered Planar Inverted-F Antenna (PIFA), the device exploits a specific RFID chip having a dual-access -wired and wireless- to the memory. A properly decoupled cell battery is also foreseen to boost the chip sensitivity and to supply power to an ultra-low power microcontroller and to the sensors. The device has been realized using off-the-shelf low-cost discrete components on FR4 substrate, validated, and tested in capturing real human movements. Two sensor-tags have been applied on the pelvis and on the torso of an individual moving in front of the RFID Reader antenna. Afterward, sensor data have been collected, processed, and filtered with specific algorithms, and used to control a musculoskeletal virtual model in the OpenSense software tool. The results show that the whole system correctly reproduces the performed movements, demonstrating the appropriateness of the proposed RFID-sensor in wireless movement capture applications.

73.5 uW Indoor-Outdoor Light Harvesting System with Global Maximum Power Point Tracking

This work introduces a light harvesting system with battery management. In contrast to relevant solutions that operate in limited ranges, the proposed system covers a wide operating input power range from 10 uW up to 300 mW. Specifically, experimental results highlight that, combined with a 73 × 94 mm flexible light harvester, it can harness light in a range from 50 LUX (indoor lighting) up to 120,000 LUX (outdoor lighting). The introduced system consists of a boost converter and an ultra-low power microcontroller (MCU). The MCU performs Global Maximum Power Point Tracking (GMPPT), using a resistor-free time-based input power sensing method, to calculate the input power of the converter, which does not interfere with the operation of the boost converter. The efficiency of the GMPPT system was evaluated with detailed experimentation, where we achieved 99.75% average GMPPT tracking efficiency while consuming only 73.5 uW at 4.2 V.

Accurate Dynamic Voltage and Frequency Scaling Measurement for Low-Power Microcontrollors in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) began to permeate all facets of life thanks to low cost, inherent intelligent-processing capability, simple installation, flexible networking and low energy consumption characteristics. Also, the evaluation of real-world applications on different platforms can solve the implementation problems and contribute to broadening the spectrum of Internet of Things (IoT) applications. The purpose of this paper is to reduce the microcontroller's average consumption of a wireless sensor node through architectural and design processes using advanced technologies. This paper provides a state-of-the-art investigation on the most up-to date Dynamic Voltage and Frequency Scaling (DVFS) techniques. A benchmark is given to choose the appropriate DVFS technique according to the type of component, the time and resources constraints, the application requirements, the expected performance level, etc. An energy-saving design is implemented using an ultra-low power microcontroller MSP430 and is based on the DVFS strategy. To efficiently quantify the consumed energy and to ensure more accuracy, a new concept is introduced, which is the normalized power to offer more accuracy. Real voltage/frequency scaling measurement were conducted. We show that a high voltage/frequency can lead to up to 57% increase of the normalized-power.

Intelligent control of micro power – Immortal machine

Abstract Energy Harvesting Systems seek to remove the batteries from electronic devices and replace them with devices that generate directly from the environment around them. This paper presents an intelligent algorithm to manage received wireless power to do useful work even though there is insufficient energy to do the work directly. The example ultralow power microcontroller discussed here is the ATtiny85 although the approach is applicable to a whole family of similar micros. The algorithm used makes intelligent decisions whether to sleep or wake according to the amount of received and stored energy. Using an adaptive strategy of this kind the amount of work can be precisely matched to the resources available to achieve maximum utilization with the objective of keeping the device alive for as long as possible subject to the satisfactory completion of a stated set of tasks.

The Implementation of a Low Power Environmental Monitoring and Soil Moisture Measurement System Based on UHF RFID.

A smart sensor label based on the integration of ultra high frequency (UHF) radio frequency identification (RFID) technology and sensors is presented. The label is composed of a semi-active system that measures temperature, light, relative humidity and gravimetric water content (GWC) in the soil. The deployed system provides a simple, cost effective solution to monitor and control the growing of plants in modern agriculture and is intended be a part of a smart wireless sensor network (WSN) for agricultural monitoring. This paper is focused on analysis and development of a moisture sensor to measure GWC. It is based on a capacitance measurement solution, the accuracy of which is enhanced using several sensor driving frequencies. Thanks to the cancellation of supply voltage variations, the modeling of the GWC sensor and readout circuit was correct. The results we measured were close to modeled values. The maximum measurement resolution of the capacitive moisture sensor was 0.07 pF. To get the GWC from measured capacitance, a scale was used to weigh the mass of water in the soil. The comparison between capacitance measurement and calculated soil GWC is presented. The RFID measurement system has energy harvesting capabilities and an ultra-low power microcontroller, which uses embedded software to control the measurement properties. The microcontroller has to choose the appropriate model depending on the measured amplitude and chosen frequency to calculate the actual voltage on the sensing capacitor.

Heterogeneous LoRa-Based Wireless Multimedia Sensor Network Multiprocessor Platform for Environmental Monitoring.

The acquisition of data in protected natural environments is subordinated to actions that do not stress the life-forms present in that environment. This is why researchers face two conflicting interests: autonomous and robust systems that minimize the physical interaction with sensors once installed, and complex enough ones to capture and process higher volumes of data. On the basis of this situation, this paper analyses the current state-of-the-art of wireless multimedia sensor networks, identifying the limitations and needs of these solutions. In this sense, in order to improve the trade-off between autonomous and computational capabilities, this paper proposes a heterogeneous multiprocessor sensor platform, consisting of an ultra-low power microcontroller and a high-performance processor, which transfers control between processors as needed. This architecture allows the shutdown of idle systems and fail-safe remote reprogramming. The sensor equipment can be adapted to the needs of the project. The deployed equipment incorporates, in addition to environmental meteorological variables, a microphone input and two cameras (visible and thermal) to capture multimedia data. In addition to the hardware description, the paper provides a brief description of how long-range (LoRa) can be used for sending large messages (such as an image or a new firmware), an economic analysis of the platform, and a study on energy consumption of the platform according to different use cases.

Energy Efficiency in IoT Networks: Integration of Reconfigurable Antennas in Ultra Low-Power Radio Platforms Based on System-on-Chip

The current state-of-the-art of Internet of Things witnesses the development of low-cost and smart-connected wireless sensor networks (WSNs) placed at the edge of clouds, enabling new services over the Internet. In these interconnected smart sensors, energy consumption is an important issue. This paper deals with decreasing energy consumption in WSN nodes by means of antennas with reconfigurability at radiofrequency. Our research has yielded two important novelties. First, the reconfiguration is accomplished with a platform comprised of a commercial ultra low-power (ULP) micro-controller and system-on-chip radio. Second, we consider explicitly the energy overhead involved in the antenna configuration process and its impact on the global energy efficiency of the system. We discuss two complementary ways of reducing the energy consumption; one by lowering the amount of transmitted power, the other by increasing the transmission rate. We employ an antenna array with a steerable beam, controlled by an algorithm embedded in the firmware of the ULP platform. This is designed for line-of-sight conditions, and the beam steering maximizes the received power. Our results show that with a common IEEE 802.11 b/g system-on-chip module and an antenna gain of 12 dB, a net power saving factor of 28% can be obtained by reducing the transmitted power, at unchanged data rate; additionally, by keeping the transmit power constant, but increasing the data rate, an energy saving from 18% to 96% can be achieved, depending on the link distance and achievable data rate. Our experiments have confirmed both routes of energy saving.

An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting

There is growing interest in being able to run neural networks on sensors, wearables and internet-of-things (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resource-constrained edge devices. To meet this need, our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection (AED). The proposed architecture is based on the gated recurrent unit ('GRU' -- introduced by Cho et al. [9]) but features optimizations that make it implementable on ultra-low power micro-controllers such as the Arm Cortex M0+. Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is demonstrated to be highly efficient and suitable for short-duration AED and keyword spotting tasks. A single eGRU cell is 60× faster and 10× smaller than a GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks of varying complexities. The practicality of eGRU is investigated in a wearable acoustic event detection application. An eGRU model is implemented and tested on the Arm Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the eGRU model compares favorably with a full precision GRU that is running on a workstation. The embedded eGRU model achieves a classification accuracy 95.3%, which is only 2% less than the full precision GRU.