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Smart and Precision Agriculture (SPA) methods and technologies, such as autonomous robots, AI/ML, sensors, and actuators, enhance farming productivity by automating the retrieval of environmental parameters and the decision-making process, while Fog- and Edge-based paradigms enable more informed and responsive practices. Unmanned Aerial Vehicles (UAVs) can autonomously inspect crops and promptly cooperate with terrestrial vehicles to perform treatments, as recently demonstrated by the EU-funded COMP4DRONES (C4D) research project, focused on the provisioning of innovative UAV technologies for civilian applications. Modern companion-equipped UAV leverage Heterogeneous Systems-on-Chip (HeSoCs) to execute complex on-board tasks. HeSoCs generally combine a general-purpose, multi-core processor with a domain-specific accelerator-rich subsystem, massively integrating application-specific accelerators. Field Programmable Gate Array (FPGA) based HeSoCs are ideal fabrics to attain high performance and energy efficiency because of their massively parallel, deeply pipelined, non-Von-Neumann processing logic and custom memory hierarchies. Automated hardware-software co-design methodologies, e.g., FPGA overlays and toolflows, largely simplify the design phases, including the optimization of the accelerator interfaces, such as the merging of redundant components to reduce area usage. In this context, our contribution consists of a System-Level Design (SLD) methodology for the design of overlay-based UAV companion computers, including a modular and scalable accelerator-rich RISC-V HeSoC, a heterogeneous software stack and an automation toolchain to generate and integrate application-specific accelerators into our overlay. Our results show three optimized overlay variants targeting an UAV-based system employed in a SPA context. Experimental results denote improvements in performance and area usage, up to 18.5% on a FPGA-based HeSoC with respect to traditional design flows.

The MYRTUS Horizon Europe project embraces the principles of the EU CloudEdgeIoT Initiative, integrating edge, fog, and cloud in a continuum of computing resources. MYRTUS intends to deliver abstractions, cognitive orchestration mechanisms, and a whole design environment to build and operate collaborative, distributed, heterogeneous systems. The goal is to provide high performance and play a crucial role in enabling energy efficiency and trustworthiness in nowadays systems.

In this paper, we present the SECURED project11Funded in part by the European Union (EU), Grant Agreement no. 10109571. Views and opinions expressed are those of the authors and do not necessarily reflect those of the EU or the Health and Digital Executive Agency. Neither the EU nor the granting authority are responsible for them., aimed at improving privacy-preserving processing of data in the health domain. The technologies developed in the project will be demonstrated in four health-related use cases and with the involvement of SME's selected through an open funding call.

Computing elements of CPSs must be flexible to ensure interoperability; and adaptive to cope with the evolving internal and external state, such as battery level and critical tasks. Cryptography is a common task needed in CPSs to guarantee private communication among different devices. In this work, we propose a reconfigurable FPGA accelerator for AES workloads with different key lengths. The accelerator architecture exploits tagged-dataflow models to support the concurrent execution of multiple threads on the same accelerator. This solution demonstrates to be more resource- and energy-efficient than a set of non-reconfigurable accelerators while keeping high performance and flexibility of execution.

Advanced computations on embedded devices are nowadays a must in any application field. Often, to cope with such a need, embedded systems designers leverage on complex heterogeneous reconfigurable platforms that offer high performance, thanks to the possibility of specializing/customizing some computing elements on board, and are usually flexible enough to be optimized at runtime. In this context, monitoring the system has gained increasing interest. Ideally, monitoring systems should be non-intrusive, serve several purposes, and provide aggregated information about the behavior of the different system components. However, current literature is not close to such ideality: For example, existing monitoring systems lack in being applicable to modern heterogeneous platforms. This work presents a hardware monitoring system that is intended to be minimally invasive on system performance and resources, composable, and capable of providing to the user homogeneous observability and transparent access to the different components of a heterogeneous computing platform, so system metrics can be easily computed from the aggregation of the collected information. Building on a previous work, this article is primarily focused on the extension of an existing hardware monitoring system to cover also specialized coprocessing units, and the assessment is done on a Xilinx FPGA-based System on Programmable Chip. Different explorations are presented to explain the level of customizability of the proposed hardware monitoring system, the tradeoffs available to the user, and the benefits with respect to standard de facto monitoring support made available by the targeted FPGA vendor.

Cyber-Physical Systems (CPS) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, smart, adaptive, predictive and react in real-time. Indeed, as sight for human beings, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis we are developing novel methods and tools to integrate complex image and video processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPS both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.

In this article, we present a new, simple, accurate, and fast power estimation technique that can be used to explore the power consumption of digital system designs at an early design stage. We exploit the machine learning techniques to aid the designers in exploring the design space of possible architectural solutions, and more specifically, their dynamic power consumption, which is application-, technology-, frequency-, and data-stimuli dependent. To model the power and the behavior of digital components, we adopt the Artificial Neural Networks (ANNs), while the final target technology is Application Specific Integrated Circuit (ASIC). The main characteristic of the proposed method, called NeuPow, is that it relies on propagating the signals throughout connected ANN models to predict the power consumption of a composite system. Besides a baseline version of the NeuPow methodology that works for a given predefined operating frequency, we also derive an upgraded version that is frequency-aware, where the same operating frequency is taken as additional input by the ANN models. To prove the effectiveness of the proposed methodology, we perform different assessments at different levels. Moreover, technology and scalability studies have been conducted, proving the NeuPow robustness in terms of these design parameters. Results show a very good estimation accuracy with less than 9% of relative error independently from the technology and the size/layers of the design. NeuPow is also delivering a speed-up factor of about 84× with respect to the classical power estimation flow.

Cyber-Physical Systems (CPS) are systems that are in feedback with their environment, possibly with humans in the loop. They are often distributed with sensors and actuators, smart, adaptive and predictive and react in real-time. Image- and video-processing pipelines are a prime source for environmental information improving the possibilities of active, relevant feedback. In such a context, FitOptiVis aims to provide end-to-end multi-objective optimization for imaging and video pipelines of CPS, with emphasis on energy and performance, leveraging on a reference architecture, supported by low-power, high-performance, smart devices, and by methods and tools for combined design-time and run-time multi-objective optimization within system and environment constraints.

Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of application domains, including speech recognition, natural language processing, and image classification. To foster their pervasive adoption in applications where low latency, privacy issues and data bandwidth are paramount, the current trend is to perform inference tasks at the edge. This requires deployment of DL algorithms on low-energy and resource-constrained computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage without adequate support and experience. In this paper, we present ALOHA, an integrated tool flow that tries to facilitate the design of DL applications and their porting on embedded heterogenous architectures. The proposed tool flow aims at automating different design steps and reducing development costs. ALOHA considers hardware-related variables and security, power efficiency, and adaptivity aspects during the whole development process, from pre-training hyperparameter optimization and algorithm configuration to deployment.

In this paper, we present a flexible, simple and accurate power modeling technique that can be used to estimate the power consumption of modern technology devices. We exploit Artificial Neural Networks for power and behavioral estimation in Application Specific Integrated Circuits. Our method, called NeuPow, relies on propagating the predictors between the connected neural models to estimate the dynamic power consumption of the individual components. As a first proof of concept, to study the effectiveness of NeuPow, we run both component level and system level tests on the Open GPDK 45 nm technology from Cadence, achieving errors below 1.5% and 9% respectively for component and system level. In addition, NeuPow demonstrated a speed up factor of 2490X.

Cyber-Physical Systems (CPS) are embedded computational collaborating devices, capable of sensing and controlling physical elements and, often, responding to humans. Designing and managing systems able to respond to different, concurrent requirements during operation is not straightforward, and introduce the need of proper support at design-time and run-time. The Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) EU project has developed a design environment for adaptive CPS. CERBERO approach leverages on model-based methodologies including different technologies and tools developed to cover design and operation from user interactions down to low level computing layer implementation.

This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system.

In the last few years, besides the concepts of embedded and interconnected systems, also the notion of Cyber-Physical Systems (CPS) has emerged: embedded computational collaborating devices, capable of sensing and controlling physical elements and, often, responding to humans. The continuous interaction between physical and computing layers makes their design and maintenance extremely complex. Uncertainty management and runtime reconfigurability, to mention the most relevant ones, are rarely tackled by available toolchains. In this context, the Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) EU project aims at developing a design environment for CPS based of two pillars: 1) a cross-layer model-based approach to describe, optimize, and analyze the system and all its different views concurrently and 2) an advanced adaptivity support based on a multi-layer autonomous engine. In this work, we describe the components and the required developments for seamless design of reusable and reconfigurable CPS and System of Systems in uncertain hybrid environments.

Advancements in CMOS technology enable the integration of a huge number of resources on the same system-on-chip. Managing the consequent growing complexity, including fault tolerance issues in deep submicron technologies, is a hard challenge for hardware designers. Self-organization may represent a viable path toward the development of massively parallel architectures in current and future technologies. This approach is progressively more studied in multiprocessor architectures where, however, a further mind-set shift in terms of programming paradigm is required. In this article, self-organization and self-adaptiveness are exploited for the design of a coprocessing unit for array computations, supporting floating-point arithmetic. From the experience of previous explorations, an architecture embodying some principle of swarm intelligence to pursue adaptability, scalability, and fault tolerance is proposed. The architecture realizes a loosely structured collection of hardware agents implementing fixed behavioral rules aimed at the best exploitation of the available resources in whatever kind of context without any hardware reconfiguration. Comparisons with off-the-shelf very long instruction word (VLIW) digital signal processors (DSPs) on specific tasks reveal similar performance thus not paying the improved robustness with performance. The multitasking capabilities, together with the intrinsic scalability, make this approach valuable for future extensions as well, especially in the field of neuronal networks simulators.

The manual creation of specialized hard-ware infrastructures for complex multi-purpose systems is error-prone and time-consuming. Moreover, lots of effort is required to define an optimized and heterogeneous components library. To tackle these issues, we propose a novel design flow based on the Dataflow Process Networks Model of Computation. In particular, we have combined the operation of two state of the art tools, the Multi-Dataflow Composer and the Open RVC-CAL Compiler, handling respectively the automatic mapping of a reconfigurable multi-purpose substrate and the high level synthesis of hardware components. Our approach guarantees runtime efficiency and on-chip area saving both on FPGAs and ASICs.

NoCs performance are usually explored stand-alone, overlooking the impact of the higher communication levels in the ISO OSI micro network stack. Nevertheless, since CPUs have to be relieved of communication management, higher communication levels such as DMA engines necessarily influence the communication performance. In this paper, we investigate how two different DMA implementations, full-duplex and half-duplex, can bias the behavior of a NoC designed for MPP architectures. From our studies, it turned out that a full-duplex DMA is more effective in preventing possible deadlock situations. Moreover, a deep performance analysis of a state-of-the-art NoC, in terms of transactions completion time, queuing time and injection delay, confirms the impact of the DMA in NoC-based MPP platforms, showing the advantages of a full-duplex approach.

As the multi-core processors era took place, several design concerns have risen. Interconnection layer efficiency has gained particular relevance as a crucial issue to be addressed in order to leverage the large amount of on-chip resources that today's VLSI technologies are able to provide. At the same time, as the architectural parallelism will continue to grow and become more fine-grained, the kind of traffic generated by the different multithreaded applications is turning out to be very wide-ranging in terms of size and burstiness. In order to adapt to this large variety of traffic to be supported, several models of dual-mode routers have been developed, implementing both packet switching and circuit switching techniques, thus supporting both best effort and guaranteed throughput services. This paper introduces an innovative model of non-exclusive dual-mode router, able to combine the aforementioned features in a non exclusive way (i.e.: in parallel inside the network on the same link). This feature makes this NoC architecture well-suited for multi-processor system on-chip (MPSoC) architectures with a high level of parallelism which have to deal with heterogeneous traffic conditions, such as massively parallel processors (MPPs) and processor arrays (PAs).