Transiently Powered Computers

Demand for compact, easily deployable, energy-efficient computers has driven the development of general-purpose transiently powered computers (TPCs) that lack both batteries and wired power, operating exclusively on energy harvested from their surroundings. TPCs\u27 dependence solely on transient, harvested power offers several important design-time benefits. For example, omitting batteries saves board space and weight while obviating the need to make devices physically accessible for maintenance. However, transient power may provide an unpredictable supply of energy that makes operation difficult. A predictable energy supply is a key abstraction underlying most electronic designs. TPCs discard this abstraction in favor of opportunistic computation that takes advantage of available resources. A crucial question is how should a software-controlled computing device operate if it depends completely on external entities for power and other resources? The question poses challenges for computation, communication, storage, and other aspects of TPC design. The main idea of this work is that software techniques can make energy harvesting a practicable form of power supply for electronic devices. Its overarching goal is to facilitate the design and operation of usable TPCs. This thesis poses a set of challenges that are fundamental to TPCs, then pairs these challenges with approaches that use software techniques to address them. To address the challenge of computing steadily on harvested power, it describes Mementos, an energy-aware state-checkpointing system for TPCs. To address the dependence of opportunistic RF-harvesting TPCs on potentially untrustworthy RFID readers, it describes CCCP, a protocol and system for safely outsourcing data storage to RFID readers that may attempt to tamper with data. Additionally, it describes a simulator that facilitates experimentation with the TPC model, and a prototype computational RFID that implements the TPC model. To show that TPCs can improve existing electronic devices, this thesis describes applications of TPCs to implantable medical devices (IMDs), a challenging design space in which some battery-constrained devices completely lack protection against radio-based attacks. TPCs can provide security and privacy benefits to IMDs by, for instance, cryptographically authenticating other devices that want to communicate with the IMD before allowing the IMD to use any of its battery power. This thesis describes a simplified IMD that lacks its own radio, saving precious battery energy and therefore size. The simplified IMD instead depends on an RFID-scale TPC for all of its communication functions. TPCs are a natural area of exploration for future electronic design, given the parallel trends of energy harvesting and miniaturization. This work aims to establish and evaluate basic principles by which TPCs can operate

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Design and Code Optimization for Systems with Next-generation Racetrack Memories

With the rise of computationally expensive application domains such as machine learning, genomics, and fluids simulation, the quest for performance and energy-efficient computing has gained unprecedented momentum. The significant increase in computing and memory devices in modern systems has resulted in an unsustainable surge in energy consumption, a substantial portion of which is attributed to the memory system. The scaling of conventional memory technologies and their suitability for the next-generation system is also questionable. This has led to the emergence and rise of nonvolatile memory ( NVM ) technologies. Today, in different development stages, several NVM technologies are competing for their rapid access to the market. Racetrack memory ( RTM ) is one such nonvolatile memory technology that promises SRAM -comparable latency, reduced energy consumption, and unprecedented density compared to other technologies. However, racetrack memory ( RTM ) is sequential in nature, i.e., data in an RTM cell needs to be shifted to an access port before it can be accessed. These shift operations incur performance and energy penalties. An ideal RTM , requiring at most one shift per access, can easily outperform SRAM . However, in the worst-cast shifting scenario, RTM can be an order of magnitude slower than SRAM . This thesis presents an overview of the RTM device physics, its evolution, strengths and challenges, and its application in the memory subsystem. We develop tools that allow the programmability and modeling of RTM -based systems. For shifts minimization, we propose a set of techniques including optimal, near-optimal, and evolutionary algorithms for efficient scalar and instruction placement in RTMs . For array accesses, we explore schedule and layout transformations that eliminate the longer overhead shifts in RTMs . We present an automatic compilation framework that analyzes static control flow programs and transforms the loop traversal order and memory layout to maximize accesses to consecutive RTM locations and minimize shifts. We develop a simulation framework called RTSim that models various RTM parameters and enables accurate architectural level simulation. Finally, to demonstrate the RTM potential in non-Von-Neumann in-memory computing paradigms, we exploit its device attributes to implement logic and arithmetic operations. As a concrete use-case, we implement an entire hyperdimensional computing framework in RTM to accelerate the language recognition problem. Our evaluation shows considerable performance and energy improvements compared to conventional Von-Neumann models and state-of-the-art accelerators

Collective Communications and Computation Mechanisms on the RF Channel for Organic Printed Smart Labels and Resource-limited IoT Nodes

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are seen as enabler technologies for realizing the Internet of Things (IoT). Organic and printed Electronics (OE) has the potential to provide low cost and all-printable smart RFID labels in high volumes. With regard to WSN, power harvesting techniques and resource-efficient communications are promising key technologies to create sustainable and for the environment friendly sensing devices. However, the implementation of OE smart labels is only allowing printable devices of ultra-low hardware complexity, that cannot employ standard RFID communications. And, the deployment of current WSN technology is far away from offering battery-free and low-cost sensing technology. To this end, the steady growth of IoT is increasing the demand for more network capacity and computational power. With respect to wireless communications research, the state-of-the-art employs superimposed radio transmission in form of physical layer network coding and computation over the MAC to increase information flow and computational power, but lacks on practicability and robustness so far. With regard to these research challenges we developed in particular two approaches, i.e., code-based Collective Communications for dense sensing environments, and time-based Collective Communications (CC) for resource-limited WSNs. In respect to the code-based CC approach we exploit the principle of superimposed radio transmission to acquire highly scalable and robust communications obtaining with it at the same time as well minimalistic smart RFID labels, that can be manufactured in high volume with present-day OE. The implementation of our code-based CC relies on collaborative and simultaneous transmission of randomly drawn burst sequences encoding the data. Based on the framework of hyper-dimensional computing, statistical laws and the superposition principle of radio waves we obtained the communication of so called ensemble information, meaning the concurrent bulk reading of sensed values, ranges, quality rating, identifiers (IDs), and so on. With 21 transducers on a small-scale reader platform we tested the performance of our approach successfully proving the scalability and reliability. To this end, we implemented our code-based CC mechanism into an all-printable passive RFID label down to the logic gate level, indicating a circuit complexity of about 500 transistors. In respect to time-based CC approach we utilize the superimposed radio transmission to obtain resource-limited WSNs, that can be deployed in wide areas for establishing, e.g., smart environments. In our application scenario for resource-limited WSN, we utilize the superimposed radio transmission to calculate functions of interest, i.e., to accomplish data processing directly on the radio channel. To prove our concept in a case study, we created a WSN with 15 simple nodes measuring the environmental mean temperature. Based on our analysis about the wireless computation error we were able to minimize the stochastic error arbitrarily, and to remove the systematic error completely

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Energy-aware design of hardware and software for ultra-low-power systems

Future visions of the Internet of Things and Industry 4.0 demand for large scale deployments of mobile devices while removing the numerous disadvantages of using batteries: degradation, scale, weight, pollution, and costs. However, this requires computing platforms with extremely low energy consumptions, and thus employ ultra-low-power hardware, energy harvesting solutions, and highly efficient power-management hardware and software. The goal of these power management solutions is to either achieve power neutrality, a condition where energy harvest and energy consumption equalize while maximizing the service quality, or to enhance power efficiency for conserving energy reserves. To reach these goals, intelligent power-management decisions are needed that utilize precise energy data. This thesis discusses the measurement of energy in embedded systems, both online and by external equipment, and the utilization of the acquired data for modeling the power consumption states of each involved hardware component. Furthermore, a method is shown to use the resulting models by instrumenting preexisting device drivers. These drivers enable new functionalities, such as online energy accounting and energy application interfaces, and facilitate intelligent power management decisions. In order to reduce additional efforts for device driver reimplementation and the violation of the separation of concerns paradigm, the approach shown in this thesis synthesizes instrumentation aspects for an aspect oriented programming language, so that the original device-driver source code remains unaffected. Eventually, an automated process of energy measurement and data analysis is presented. This process is able to yield precise energy models with low manual effort. In combination with the instrumentation synthesis of aspect code, this method enables an accelerated creation process for energy models of ultra-low-power systems. For all proposed methods, empirical accuracy and overhead measurements are presented. To support the claims of the author, first practical energy aware and wireless-radio networked applications are showcased: An energy-neutral light sensor, a photovoltaic-powered seminar-room door plate, and a sensor network experiment testbed for research and education

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues