Train me if you can: decentralized learning on the deep edge

The end of Moore’s Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the training remains confined to the cloud, imposing the transaction of high volumes of private data over a network and leading to unpredictable delays when ML applications attempt to adapt to adversarial environments. To fill this gap, we make the first attempt to evaluate the feasibility of ANN training in Arm Cortex-M MCUs. From the available optimization algorithms, stochastic gradient descent (SGD) has the best trade-off between accuracy, memory footprint, and latency. However, its original form and the variants available in the literature still do not fit the stringent requirements of Arm Cortex-M MCUs. We propose L-SGD, a lightweight implementation of SGD optimized for maximum speed and minimal memory footprint in this class of MCUs. We developed a floating-point version and another that operates over quantized weights. For a fully-connected ANN trained on the MNIST dataset, L-SGD (float-32) is 4.20× faster than the SGD while requiring only 2.80% of the memory with negligible accuracy loss. Results also show that quantized training is still unfeasible to train an ANN from the scratch but is a lightweight solution to perform minor model fixes and counteract the fairness problem in typical FL systems.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by FCT within the PhD Scholarship Project Scope: SFRH/BD/146780/2019

Shifting capsule networks from the cloud to the deep edge

Capsule networks (CapsNets) are an emerging trend in image processing. In contrast to a convolutional neural network, CapsNets are not vulnerable to object deformation, as the relative spatial information of the objects is preserved across the network. However, their complexity is mainly related to the capsule structure and the dynamic routing mechanism, which makes it almost unreasonable to deploy a CapsNet, in its original form, in a resource-constrained device powered by a small microcontroller (MCU). In an era where intelligence is rapidly shifting from the cloud to the edge, this high complexity imposes serious challenges to the adoption of CapsNets at the very edge. To tackle this issue, we present an API for the execution of quantized CapsNets in Arm Cortex-M and RISC-V MCUs. Our software kernels extend the Arm CMSIS-NN and RISC-V PULP-NN to support capsule operations with 8-bit integers as operands. Along with it, we propose a framework to perform post-training quantization of a CapsNet. Results show a reduction in memory footprint of almost 75%, with accuracy loss ranging from 0.07% to 0.18%. In terms of throughput, our Arm Cortex-M API enables the execution of primary capsule and capsule layers with medium-sized kernels in just 119.94 and 90.60 milliseconds (ms), respectively (STM32H755ZIT6U, Cortex-M7 @ 480 MHz). For the GAP-8 SoC (RISC-V RV32IMCXpulp @ 170 MHz), the latency drops to 7.02 and 38.03 ms, respectively

A kernel clustering algorithm based on diameters

This paper analyzes an iterative kernel partitioning clustering algorithm that dynamically merges, removes and adds clusters using some characteristics, like the radii and diameters of the clusters, and distance between centers. The clustering is carried out in feature space in terms of a kernel function so that non-linearly separable clusters are identified. The preliminary experiments with seven datasets show that the proposed algorithm is able to successfully converge to the expected clustering. It is also shown that the algorithm performance is sensitive to the parameter σ of the Gaussian kernel.This work has been supported by FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/00013/2020 and UIDP/00013/2020 of CMAT-U

COOPEDU IV — Cooperação e Educação de Qualidade

O quarto Congresso Internacional de Cooperação e Educação-IV COOPEDU, organizado pelo Centro de Estudos Internacionais (CEI) do Instituto Universitário de Lisboa e pela Escola Superior de Educação e Ciências Sociais do Instituto Politécnico de Leiria decorreu nos dias 8 e 9 de novembro de 2018, subordinado à temática Cooperação e Educação de Qualidade. Este congresso insere-se numa linha de continuidade de intervenção por parte das duas instituições organizadoras e dos elementos coordenadores e este ano beneficiou do financiamento do Instituto Camões, obtido através de um procedimento concursal, que nos permitiu contar com a participação presencial de elementos dos Países Africanos de Língua Portuguesa, fortemente implicados nas problemáticas da Educação e da Formação. Contou também com a participação do Instituto Camões e da Fundação Calouste Gulbenkian, entidades que sistematizaram a sua intervenção nos domínios da cooperação na área da educação nos últimos anos. A opção pela temática da qualidade pareceu aos organizadores pertinente e actual. Com efeito os sistemas educativos dos países que constituem a Comunidade de países de língua portuguesa têm implementado várias reformas mas em vários domínios mantem-se a insatisfação de responsáveis políticos, pedagogos, técnicos sociais face aos resultados obtidos. Aliás o caminho de procura da Qualidade é interminável porque vai a par da aposta na exigência e na promoção da cidadania e responsabilidade social. As comunicações que agora se publicam estão organizadas em dois eixos: o das Políticas da Educação e Formação e o das dimensões em que se traduzem essas políticas. Neste último eixo encontramos fios condutores para agregarmos as comunicações apresentadas

A driver monitoring system towards the automotive cockpit of the future

Dissertação de mestrado integrado em Industrial Electronics and Computers EngineeringDespite the massive technological evolution experienced by the automotive industry over the past decades, driver safety is still an area of big concern. The lack of attention during the driving task is, till nowadays, considered as a major risk factor for fatal road accidents around the world. Actually, the motor vehicle traffic crashes are among the leading causes of death in some countries, like United States of America. Most of the registered accidents arose due to driver fatigue or distraction. In the near future, the paradigm shift from manual to autonomous driving will impose new serious challenges, increasing substantially the range of concerns in the automotive industry. Notwithstanding the significant contribution of this technology to long-term road safety, the fact is that all vehicles available at the moment only feature semi-autonomous technology, demanding frequent driver action. In reality, the current technology has a limited operational design domain, requiring the driver to retake control over the vehicle in a short amount of time, as soon as the automation limits are reached. However, the monotony of such scenario may lead the driver to engage in non-driving related activities or even induce fatigue, reducing his/her driving awareness and degrading the quality of the fallback operation. In this context, this MSc thesis proposes a system to monitor the driver in terms of fatigue, distraction and activity. Regarding the fatigue assessment, it must detect microsleeps and situations of drowsiness. In terms of distraction, the following types must be supervised: (i) visual, (ii) manual and (iii) cognitive. The activity monitoring module must recognize four out of ten deadliest driving non-related activities: (i) using cell phone, (ii) looking to an external event, (iii) interacting with the infotainment and (iv) interacting with passengers. This system will be part of an automotive HMI, developed under the Bosch InnovCar project, which aims to develop new solutions for the cockpit of the future.Apesar da enorme evolução tecnológica sofrida pela indústria automóvel nas últimas décadas, a segurança rodoviária ainda é uma área de preocupação. A falta de atenção durante a condução é, até aos dias de hoje, considerada um fator de risco elevado para a ocorrência de acidentes rodoviários fatais. Estes estão mesmo entre as principais causas de morte em vários países, incluindo os Estados Unidos da América. Acredita-se que um número considerável dos acidentes registados surjam como resultado de fadiga ou distração do condutor. Além disso, o atual paradigma de transição para condução autónoma também impõe sérios desafios. Não obstante o contributo desta tecnologia para a segurança rodoviária a longo prazo, a verdade é que os veículos atuais apenas apresentam tecnologia semi-autónoma, exigindo a ação frequente do condutor. De facto, a tecnologia existente tem um domínio de operação limitado, requerendo que o condutor assuma o controlo do veículo num curto intervalo de tempo, assim que os limites de automação são atingidos. No entanto, a monotonia de tal cenário pode levar à prática de atividades não relacionadas com a condução, ou até induzir fadiga, reduzindo a consciência acerca do cenário de condução e degradando a qualidade da operação de fallback. Neste contexto, esta dissertação propõe o desenvolvimento de um sistema capaz de supervisionar o condutor em termos de fadiga, distração e atividade. Relativamente ao supervisionamento de fadiga, este deve ser capaz de detetar microsleeps e situações de sonolência. Em termos de distração, os seguintes tipos devem ser supervisionados: (i) visual, (ii) manual e (iii) cognitivo. Por fim, o sistema deve reconhecer quatro das dez atividades distrativas mais letais: (i) uso de telemóvel, (ii) observação de um evento externo, (iii) interação com o sistema de multimédia e (iv) interação com os passageiros. Este sistema fará parte de um HMI automóvel, desenvolvido no âmbito do projeto Bosch InnovCar, que visa apresentar novas soluções para o cockpit do futuro.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) Project nº 002797; Funding Reference: POCI-01-0247-FEDER-00279

Detecting driver's fatigue, distraction and activity using a non-intrusive AI-based monitoring system

The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today's vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle's control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle's automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver's state with an accuracy ranging from 89% to 93%.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) Project no 002797; Funding Reference: POCI-01- 0247-FEDER-00279

Wall screen: An ultra-high definition video-card for the internet of things

8 k ultra-high definition (UHD) is paving the way for the next-generation video systems. In the audiovisual industry, besides delivering a more immersive experience, it is a mean to smooth spatial artifacts during video sampling. In the medical industry, it may provide surgeons with increased reality in surgeries such as endoscopy. Nevertheless, researchers are struggling to meet the high throughput required by this resolution and hardware solutions miss the flexibility required for on-demand updates. In this context, we propose an 8 k video-card based on a hybrid platform endowed with soft programmable logic and hard processors. The former accelerates the video pipeline from capture to encoding and playback-via SDI or PCIe. The later connects with a cloud to stream video and interfaces with the user. Results showed that our platform outputs 8 k UHD video in YUV 4:2:2 10-bits and H.264 formats at 60 and 30 frames per second, respectively.This work was supported in part by: Euro pean Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (PORTUGAL 2020) Project no 017891; Funding Reference: POCI-01-0247-FEDER-017891 and in part by FCT within the Project Scope: UID/CEC/00319/2019

The future of low-end motes in the Internet of things: a prospective paper

Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device’s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow’s IoT devices.This research was funded by FCT-Fundacao para a Ciencia e Tecnologia grant number UID/CEC/00319/2019. The APC was funded by FCT