A Cross-Disciplinary Outlook of Directions and Challenges in Industrial Electronics

[EN] How to build a sustainable society in view of industrial electronics has been discussed from energy, information and communication technologies, cyber-physical systems (CPSs), and other viewpoints. This paper presents a cross-disciplinary view that integrates the fields of human factors, professional education, electronic systems on chip, resilience and security for industrial applications, technology ethics and society, and standards. After explaining the efforts and challenges in these fields, this paper shows a methodology for cross-disciplinary technology that integrates the technical committees in Cluster 4, Industrial Electronics Society. A project, which was launched in March 2022, implements a 'Proof of Concept' trial of the methodology.The work of Jinhua Sh was supported by JSPS Grant-in-Aid for Scientific Research B under Grant 22H03998 (Japan).She, J.; Guzman-Miranda, H.; Huang, V.; Chen, AC.; Karnouskos, S.; Dunai, L.; Ma, C.... (2022). A Cross-Disciplinary Outlook of Directions and Challenges in Industrial Electronics. IEEE Journal of Emerging and Selected Topics in Industrial Electronics (Online). 3:375-391. https://doi.org/10.1109/OJIES.2022.3174218375391

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Heterogeneous Acceleration for 5G New Radio Channel Modelling Using FPGAs and GPUs

L'abstract è presente nell'allegato / the abstract is in the attachmen

AHEAD: Automatic Holistic Energy-Aware Design Methodology for MLP Neural Network Hardware Generation in Proactive BMI Edge Devices

The prediction of a high-level cognitive function based on a proactive brain–machine interface (BMI) control edge device is an emerging technology for improving the quality of life for disabled people. However, maintaining the stability of multiunit neural recordings is made difficult by the nonstationary nature of neurons and can affect the overall performance of proactive BMI control. Thus, it requires regular recalibration to retrain a neural network decoder for proactive control. However, retraining may lead to changes in the network parameters, such as the network topology. In terms of the hardware implementation of the neural decoder for real-time and low-power processing, it takes time to modify or redesign the hardware accelerator. Consequently, handling the engineering change of the low-power hardware design requires substantial human resources and time. To address this design challenge, this work proposes AHEAD: an automatic holistic energy-aware design methodology for multilayer perceptron (MLP) neural network hardware generation in proactive BMI edge devices. By taking a holistic analysis of the proactive BMI design flow, the approach makes judicious use of the intelligent bit-width identification (BWID) and configurable hardware generation, which autonomously integrate to generate the low-power hardware decoder. The proposed AHEAD methodology begins with the trained MLP parameters and golden datasets and produces an efficient hardware design in terms of performance, power, and area (PPA) with the least loss of accuracy. The results show that the proposed methodology is up to a 4X faster in performance, 3X lower in terms of power consumption, and achieves a 5X reduction in area resources, with exact accuracy, compared to floating-point and half-floating-point design on a field-programmable gate array (FPGA), which makes it a promising design methodology for proactive BMI edge devices

A sensor node soC architecture for extremely autonomous wireless sensor networks

Tese de Doutoramento em Engenharia Eletrónica e de Computadores (PDEEC) (especialidade em Informática Industrial e Sistemas Embebidos)The Internet of Things (IoT) is revolutionizing the Internet of the future and the way new smart objects and people are being connected into the world. Its pervasive computing and communication technologies connect myriads of smart devices, presented at our everyday things and surrounding objects. Big players in the industry forecast, by 2020, around 50 billion of smart devices connected in a multitude of scenarios and heterogeneous applications, sharing data over a true worldwide network. This will represent a trillion dollar market that everyone wants to take a share. In a world where everything is being connected, device security and device interoperability are a paramount. From the sensor to the cloud, this triggers several technological issues towards connectivity, interoperability and security requirements on IoT devices. However, fulfilling such requirements is not straightforward. While the connectivity exposes the device to the Internet, which also raises several security issues, deploying a standardized communication stack on the endpoint device in the network edge, highly increases the data exchanged over the network. Moreover, handling such ever-growing amount of data on resource-constrained devices, truly affects the performance and the energy consumption. Addressing such issues requires new technological and architectural approaches to help find solutions to leverage an accelerated, secure and energy-aware IoT end-device communication. Throughout this thesis, the developed artifacts triggered the achievement of important findings that demonstrate: (1) how heterogeneous architectures are nowadays a perfect solution to deploy endpoint devices in scenarios where not only (heavy processing) application-specific operations are required, but also network-related capabilities are major concerns; (2) how accelerating network-related tasks result in a more efficient device resources utilization, which combining better performance and increased availability, contributed to an improved overall energy utilization; (3) how device and data security can benefit from modern heterogeneous architectures that rely on secure hardware platforms, which are also able to provide security-related acceleration hardware; (4) how a domain-specific language eases the co-design and customization of a secure and accelerated IoT endpoint device at the network edge.Internet of Things (IoT) é o conceito que está a revolucionar a Internet do futuro e a forma como coisas, processos e pessoas se conectam e se relacionam numa infraestrutura de rede global que interligará, num futuro próximo, um vasto número de dispositivos inteligentes e de utilização diária. Com uma grande aposta no mercado IoT por parte dos grandes líderes na industria, algumas visões otimistas preveem para 2020 mais de 50 mil milhões de dispositivos ligados na periferia da rede, partilhando grandes volumes de dados importantes através da Internet, representando um mercado multimilionário com imensas oportunidades de negócio. Num mundo interligado de dispositivos, a interoperabilidade e a segurança é uma preocupação crescente. Tal preocupação exige inúmeros esforços na exploração de novas soluções, quer a nível tecnológico quer a nível arquitetural, que visem impulsionar o desenvolvimento de dispositivos embebidos com maiores capacidades de desempenho, segurança e eficiência energética, não só apenas do dispositivo em si, mas também das camadas e protocolos de rede associados. Apesar da integração de pilhas de comunicação e de protocolos standard das camadas de rede solucionar problemas associados à conectividade e a interoperabilidade, adiciona a sobrecarga inerente dos protocolos de comunicação e do crescente volume de dados partilhados entre os dispositivos e a Internet, afetando severamente o desempenho e a disponibilidade do mesmo, refletindo-se num maior consumo energético global. As soluções apresentadas nesta tese permitiram obter resultados que demonstram: (1) a viabilidade de soluções heterogéneas no desenvolvimento de dispositivos IoT, onde não só tarefas inerentes à aplicação podem ser aceleradas, mas também tarefas relacionadas com a comunicação do dispositivo; (2) os benefícios da aceleração de tarefas e protocolos da pilha de rede, que se traduz num melhor desempenho do dispositivo e aumento da disponibilidade do mesmo, contribuindo para uma melhor eficiência energética; (3) que plataformas de hardware modernas oferecem mecanismos de segurança que podem ser utilizados não apenas em prol da segurança do dispositivo, mas também nas capacidades de comunicação do mesmo; (4) que o desenvolvimento de uma linguagem de domínio específico permite de forma mais eficaz e eficiente o desenvolvimento e configuração de dispositivos IoT inteligentes.This thesis was supported by a PhD scholarship from Fundação para a Ciência e Tecnologia, SFRH/BD/90162/201

Area and Power Efficient FFT/IFFT Processor for FALCON Post-Quantum Cryptography

Quantum computing is an emerging technology on the verge of reshaping industries, while simultaneously challenging existing cryptographic algorithms. FALCON, a recent standard quantum-resistant digital signature, presents a challenging hardware implementation due to its extensive non-integer polynomial operations, necessitating FFT over the ring $\mathbb{Q}[x]/(x^n+1)$. This paper introduces an ultra-low power and compact processor tailored for FFT/IFFT operations over the ring, specifically optimized for FALCON applications on resource-constrained edge devices. The proposed processor incorporates various optimization techniques, including twiddle factor compression and conflict-free scheduling. In an ASIC implementation using a 22 nm GF process, the proposed processor demonstrates an area occupancy of 0.15 mm$^2$ and a power consumption of 12.6 mW at an operating frequency of 167 MHz. Since a hardware implementation of FFT/IFFT over the ring is currently non-existent, the execution time achieved by this processor is compared to the software implementation of FFT/IFFT of FALCON on a Raspberry Pi 4 with Cortex-A72, where the proposed processor achieves a speedup of up to 2.3$\times$. Furthermore, in comparison to dedicated state-of-the-art hardware accelerators for classic FFT, this processor occupies 42\% less area and consumes 83\% less power, on average. This suggests that the proposed hardware design offers a promising solution for implementing FALCON on resource-constrained devices.Comment: 14 page