Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Embedding Video Object Detection Capabilities on Low-Power Nano-Drones

Our objective for this thesis work was the deployment of a Neural Network based approach for video object detection on board a nano-drone. Furthermore, we have studied some possible extensions to exploit the temporal nature of videos to improve the detection capabilities of our algorithm. For our project, we have utilized the Mobilenetv2/v3SSDLite due to their limited computational and memory requirements. We have trained our networks on the IMAGENET VID 2015 dataset and to deploy it onto the nano-drone we have used the NNtool and Autotiler tools by GreenWaves. To exploit the temporal nature of video data we have tried different approaches: the introduction of an LSTM based convolutional layer in our architecture, the introduction of a Kalman filter based tracker as a postprocessing step to augment the results of our base architecture. We have obtain a total improvement in our performances of about 2.5 mAP with the Kalman filter based method(BYTE). Our detector run on a microcontroller class processor on board the nano-drone at 1.63 fps.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.

A multiplying-by-two CMOS amplifier for high-speed ADCs based on parametric amplification

15th International Conference on Mixed Design of Integrated Circuits and Systems, pp. 177 – 180, Poznan, Polónia

Modeling, design and experimental characterization of Micro-Electro-Mechanical-Systems for gas chromatographic applications

Design parameters, process flows, electro-thermal-fluidic simulations and experimental characterizations of Micro-Electro-Mechanical-Systems (MEMS) suited for gas-chromatographic (GC) applications are presented and thoroughly described in this thesis, whose topic belongs to the research activities the Institute for Microelectronics and Microsystems (IMM)-Bologna is involved since several years, i.e. the development of micro-systems for chemical analysis, based on silicon micro-machining techniques and able to perform analysis of complex gaseous mixtures, especially in the field of environmental monitoring. In this regard, attention has been focused on the development of micro-fabricated devices to be employed in a portable mini-GC system for the analysis of aromatic Volatile Organic Compounds (VOC) like Benzene, Toluene, Ethyl-benzene and Xylene (BTEX), i.e. chemical compounds which can significantly affect environment and human health because of their demonstrated carcinogenicity (benzene) or toxicity (toluene, xylene) even at parts per billion (ppb) concentrations. The most significant results achieved through the laboratory functional characterization of the mini-GC system have been reported, together with in-field analysis results carried out in a station of the Bologna air monitoring network and compared with those provided by a commercial GC system. The development of more advanced prototypes of micro-fabricated devices specifically suited for FAST-GC have been also presented (silicon capillary columns, Ultra-Low-Power (ULP) Metal OXide (MOX) sensor, Thermal Conductivity Detector (TCD)), together with the technological processes for their fabrication. The experimentally demonstrated very high sensitivity of ULP-MOX sensors to VOCs, coupled with the extremely low power consumption, makes the developed ULP-MOX sensor the most performing metal oxide sensor reported up to now in literature, while preliminary test results proved that the developed silicon capillary columns are capable of performances comparable to those of the best fused silica capillary columns. Finally, the development and the validation of a coupled electro-thermal Finite Element Model suited for both steady-state and transient analysis of the micro-devices has been described, and subsequently implemented with a fluidic part to investigate devices behaviour in presence of a gas flowing with certain volumetric flow rates.

Sviluppo di un firmware per un sistema di sensing impedenziometrico a bassa potenza

Il progetto vuole realizzare un sistema ultra low power, in grado di monitorare variabili fisiche quali temperatura e conducibilità dell'acqua nelle profondità marine in autonomia, per una durata complessiva di due anni. Il salvataggio dei dati raccolti nel periodo di utilizzo avrà come fine ultimo lo studio dei cambiamenti climatici relativi all'ambiente marino. Volendo collocare il sistema di monitoraggio sul dorso di pesci o in profondità oceaniche non facilmente accessibili è necessario garantire dimensioni ridotte e un funzionamento autonomo duraturo al termine del quale sarà possibile scaricare i dati raccolti. Nel tentativo di rispettare la specifica relativa al ciclo di lavoro autonomo del sistema è stato importante adottare una politica rigorosa riguardante i consumi estremamente ridotti, senza però venir meno alle ulteriori specifiche di progetto, riportate in dettaglio nei paragrafi successivi. Dalla progettazione circuitale alla realizzazione del firmware, passando per una minuziosa scelta della componentistica a minor consumo, ho avuto la possibilità di dar vita all'intero progetto in autonomia, confrontandomi con tutti gli aspetti e le problematiche che la realizzazione di un simile progetto porta con se.

Immobilizing bacteriorhodopsin on a single electron transistor

As awareness of potential human and environmental impacts from toxins has increased, so has the development of innovative sensors. Bacteriorhodopsin (bR) is a light activated proton pump contained in the purple membrane (PM) of the bacteria Halobacterium salinarum. Bacteriorhodopsin is a robust protein which can function in both wet and dry states and can withstand extreme environmental conditions. A single electron transistor(SET) is a nano-scale device that exploits the quantum mechanical properties of electrons to switch on and off. SETs have tremendous potential in practical applications due to their size, ultra low power requirements, and electrometer-like sensitivity. The main goal of this research was to create a bionanohybrid device by integrating bR with a SET device. This was achieved by a multidisciplinary approach. The SET devices were created by a combination of sputtering, photolithography, and focused ion beam machining. The bionanomaterial bacteriorhodopsin was created through oxidative fermentation and a series of transmembrane purification processes. The bR was then integrated with the SET by electrophoretic deposition, creating a bionanohybrid device. The bionanohybrid device was then characterized using a semiconductor parametric analyzer. Characterization demonstrated that the bR modulated the operational characteristics of the SET when bR was activated with light within its absorbance spectrum. To effectively integrate bacteriorhodopsin with microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), it is critical to know the electrical properties of the material and to understand how it will affect the functionality of the device. Tests were performed on dried films of bR to determine if there is a relationship between inductance, capacitance, and resistance (LCR) measurements and orientation, light-on/off, frequency, and time. The results indicated that the LCR measurements of the bR depended on the thickness and area of the film, but not on the orientation, as with other biological materials such as muscle. However, there was a transient LCR response for both oriented and unoriented bR which depended on light intensity. From the impedance measurements an empirical model was suggested for the bionanohybrid device. The empirical model is based on the dominant electrical characteristics of the bR which were the parallel capacitance and resistance. The empirical model suggests that it is possible to integrate bR with a SET without influencing its functional characteristics.

Développement de procédés technologiques pour une intégration 3D monolithique de dispositifs nanoélectroniques sur CMOS

Résumé : Le transistor monoélectronique (SET) est un dispositif nanoélectronique très attractif à cause de son ultra-basse consommation d’énergie et sa forte densité d’intégration, mais il n’a pas les capacités suffisantes pour pouvoir remplacer complètement la technologie CMOS. Cependant, la combinaison de la technologie SET avec celle du CMOS est une voie intéressante puisqu’elle permet de profiter des forces de chacune, afin d’obtenir des circuits avec des fonctionnalités additionnelles et uniques. Cette thèse porte sur l’intégration 3D monolithique de nanodispositifs dans le back-end-of-line (BEOL) d’une puce CMOS. Cette approche permet d’obtenir des circuits hybrides et de donner une valeur ajoutée aux puces CMOS actuelles sans altérer le procédé de fabrication du niveau des transistors MOS. L’étude se base sur le procédé nanodamascène classique développé à l’UdeS qui a permis la fabrication de dispositifs nanoélectroniques sur un substrat de SiO2. Ce document présente les travaux réalisés sur l’optimisation du procédé de fabrication nanodamascène, afin de le rendre compatible avec le BEOL de circuits CMOS. Des procédés de gravure plasma adaptés à la fabrication de nanostructures métalliques et diélectriques sont ainsi développés. Le nouveau procédé nanodamascène inverse a permis de fabriquer des jonctions MIM et des SET métalliques sur une couche de SiO2. Les caractérisations électriques de MIM et de SET formés avec des jonctions TiN/Al2O3 ont permis de démontrer la présence de pièges dans les jonctions et la fonctionnalité d’un SET à basse température (1,5 K). Le transfert de ce procédé sur CMOS et le procédé d’interconnexions verticales sont aussi développés par la suite. Finalement, un circuit 3D composé d’un nanofil de titane connecté verticalement à un transistor MOS est réalisé et caractérisé avec succès. Les résultats obtenus lors de cette thèse permettent de valider la possibilité de co-intégrer verticalement des dispositifs nanoélectroniques avec une technologie CMOS, en utilisant un procédé de fabrication compatible.

Nodi sensori LoRaWAN a micropotenze: progettazione hardware, firmware e a livello di applicazione

L'elaborato parte da una scheda di sviluppo commerciale per arrivare a realizzare una rete LoRaWAN comprensiva di End-Node, Gateway e Application Server. In maniera specifica, l'elaborato affronta il problema della progettazione di end-node a micropotenze. Dopo aver studiato e collaudato la piattaforma di sviluppo, è stata affrontata la problematica dell'ottimizzazione energetica a diversi livelli: scelta di componenti con correnti di perdita estremamente ridotte, tecniche di power gating temporizzato, comportamento adattativo del nodo, impostazione dei consumi del nodo mediante i server della rete. L'elaborato presenta infine il layout del PCB progettato, pronto per la fabbricazione, insieme a stime del tempo di vita dell'end-node in funzione della frequenza di trasmissione e della capacità delle batterie utilizzate.

On the optimum parameters of a device for harvesting energy from running and walking

This paper investigates the feasibility of using an energy harvesting device tuned such that its natural frequency coincides with higher harmonics of the input to capture energy from walking or running human motion more efficiently. The paper starts by reviewing the concept of a linear resonant generator for a tonal frequency input and then derives an expression for the power harvested for an input with several harmonics. The amount of power harvested is estimated numerically using measured data from human subjects. Assuming that the input is periodic, the signal is reconstructed using a Fourier series before being used in the simulation. It is found that although the power output depends on the input frequency, the choice of tuning the natural frequency of the device to coincide with a particular higher harmonic is restricted by the amount of damping that is needed to maximize the amount of power harvested, as well as to comply with the size limit of the device. It is also found that it is not feasible to tune the device to match the first few harmonics when the size of the device is small, because a large amount of damping is required to limit the motion of the mass.

Sistema de extração de potência (power harvesting) usando transdutores piezelétricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Hybrid Power System Intelligent Operation and Protection Involving Plug-in Electric Vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Planning smart cities using wireless low energy monitoring systems

Thesis to obtain the Master Degree in Electronics and Telecommunications Engineering

Energy evaporation: the new concept of indoor systems for WPT and EH embedded into the floor

This work introduces a novel idea for wireless energy transfer, proposing for the first time the unit-cell of an indoor localization and RF harvesting system embedded into the floor. The unit-cell is composed by a 5.8 GHz patch antenna surrounded by a 13.56 MHz coil. The coil locates a device and activate the patch which, connected to a power grid, radiates to wirelessly charge the localized device. The HF and RF circuits co-existence and functionality are demonstrated in this paper, the novelty of which is also in the adoption of low cost and most of all ecofriendly materials, such as wood and cork, as substrates for electronics.