Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

Combined on-line lifetime-energy optimization for asymmetric multicores

In this paper we present an architectural and on-line resource management solution to optimize lifetime reliability of asymmetric multicores while minimizing the system energy consumption, targeting both single nodes (multicores) as well as multiple ones (cluster of multicores). The solution exploits the different characteristics of the computing resources to achieve the desired performance while optimizing the lifetime/energy trade-off. The experimental results show that a combined optimization of energy and lifetime allows for achieving an extended lifetime (similar to the one pursued by lifetime-only optimization solutions) with a marginal energy consumption detriment (less than 2%) with respect to energy-aware but aging-unaware systems

Embedded Virtual Machines for Robust Wireless Control Systems

Embedded wireless networks have largely focused on open loop sensing and monitoring. To address actuation in closed loop wireless control systems there is a strong need to re-think the communication architectures and protocols for reliability, coordination and control. As the links, nodes and topology of wireless systems are inherently unreliable, such time-critical and safety-critical applications require programming abstractions where the tasks are assigned to the sensors, actuators and controllers as a single component rather than statically mapping a set of tasks to a specific physical node at design time. To this end, we introduce the Embedded Virtual Machine (EVM), a powerful and flexible programming abstraction where virtual components and their properties are maintained across node boundaries. In the context of process and discrete control, an EVM is the distributed runtime system that dynamically selects primary-backup sets of controllers to guarantee QoS given spatial and temporal constraints of the underlying wireless network. The EVM architecture defines explicit mechanisms for control, data and fault communication within the virtual component. EVM-based algorithms introduce new capabilities such as predictable outcomes and provably minimal graceful degradation during sensor/actuator failure, adaptation to mode changes and runtime optimization of resource consumption. Through the design of a natural gas process plant hardware-in-loop simulation we aim to demonstrate the preliminary capabilities of EVM-based wireless networks

CoAP Infrastructure for IoT

The Internet of Things (IoT) can be seen as a large-scale network of billions of smart devices. Often IoT devices exchange data in small but numerous messages, which requires IoT services to be more scalable and reliable than ever. Traditional protocols that are known in the Web world does not fit well in the constrained environment that these devices operate in. Therefore many lightweight protocols specialized for the IoT have been studied, among which the Constrained Application Protocol (CoAP) stands out for its well-known REST paradigm and easy integration with existing Web. On the other hand, new paradigms such as Fog Computing emerges, attempting to avoid the centralized bottleneck in IoT services by moving computations to the edge of the network. Since a node of the Fog essentially belongs to relatively constrained environment, CoAP fits in well. Among the many attempts of building scalable and reliable systems, Erlang as a typical concurrency-oriented programming (COP) language has been battle tested in the telecom industry, which has similar requirements as the IoT. In order to explore the possibility of applying Erlang and COP in general to the IoT, this thesis presents an Erlang based CoAP server/client prototype ecoap with a flexible concurrency model that can scale up to an unconstrained environment like the Cloud and scale down to a constrained environment like an embedded platform. The flexibility of the presented server renders the same architecture applicable from Fog to Cloud. To evaluate its performance, the proposed server is compared with the mainstream CoAP implementation on an Amazon Web Service (AWS) Cloud instance and a Raspberry Pi 3, representing the unconstrained and constrained environment respectively. The ecoap server achieves comparable throughput, lower latency, and in general scales better than the other implementation in the Cloud and on the Raspberry Pi. The thesis yields positive results and demonstrates the value of the philosophy of Erlang in the IoT space

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Runtime adaptive iomt node on multi-core processor platform

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continuous patient monitoring. Two very important aspects should be considered to make the most of this paradigm. For the first aspect, moving the processing task from the cloud to the edge leads to several advantages, such as responsiveness, portability, scalability, and reliability of the sensor node. For the second aspect, in order to increase the accuracy of the system, state-of-the-art cognitive algorithms based on artificial intelligence and deep learning must be integrated. Sensory nodes often need to be battery powered and need to remain active for a long time without a different power source. Therefore, one of the challenges to be addressed during the design and development of IoMT devices concerns energy optimization. Our work proposes an implementation of cognitive data analysis based on deep learning techniques on resource-constrained computing platform. To handle power efficiency, we introduced a component called Adaptive runtime Manager (ADAM). This component takes care of reconfiguring the hardware and software of the device dynamically during the execution, in order to better adapt it to the workload and the required operating mode. To test the high computational load on a multi-core system, the Orlando prototype board by STMicroelectronics, cognitive analysis of Electrocardiogram (ECG) traces have been adopted, considering single-channel and six-channel simultaneous cases. Experimental results show that by managing the sensory node configuration at runtime, energy savings of at least 15% can be achieved