593 research outputs found
SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS
The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph
The Impact of Lithium Ion on the Application of Resistive Switching Devices
With the development of the times, people have higher and higher requirements for
storage equipment. Many new storage devices have emerged, such as Magnetoresistive
random-access memory(MRAM) and Resistive random-access memory(ReRAM). The
junction structure is the basic unit of these two storage devices, and in this paper, the
MTJ and resistive switching junction are tuned with lithium fluoride(LiF) to optimize
their performance, respectively.
In the first experiment, a magnetic tunnelling junction resembling a battery is
developed and proved to be electromagnetically tuneable. In this LiF-based device, reversible
non-volatile resistive switching phenomena and tunnelling phenomena coexist,
enabling four well-defined groups for each device. The management of the interface enables
the spin transfer of actively controlled devices, hence enhancing their application
potential.
In the second experiment, 796 devices were measured. For the resistive switching
device with TiO as the insulating layer, adding additional LiF layer can significantly
increase the probability of resistive switching phenomenon, and adding an appropriate
thickness of LiF can also increase the differentiation between high and low group states,
which is beneficial for the regulation of resistive switching devices
Wearable sensors for respiration monitoring: a review
This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects
TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
The challenging deployment of compute-intensive applications from domains
such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces
the community of computing systems to explore new design approaches.
Approximate Computing appears as an emerging solution, allowing to tune the
quality of results in the design of a system in order to improve the energy
efficiency and/or performance. This radical paradigm shift has attracted
interest from both academia and industry, resulting in significant research on
approximation techniques and methodologies at different design layers (from
system down to integrated circuits). Motivated by the wide appeal of
Approximate Computing over the last 10 years, we conduct a two-part survey to
cover key aspects (e.g., terminology and applications) and review the
state-of-the art approximation techniques from all layers of the traditional
computing stack. In Part II of our survey, we classify and present the
technical details of application-specific and architectural approximation
techniques, which both target the design of resource-efficient
processors/accelerators & systems. Moreover, we present a detailed analysis of
the application spectrum of Approximate Computing and discuss open challenges
and future directions.Comment: Under Review at ACM Computing Survey
Toward Fault-Tolerant Applications on Reconfigurable Systems-on-Chip
L'abstract è presente nell'allegato / the abstract is in the attachmen
Pressure sensing of silicon nanowires
Nanostructures made from crystalline silicon, especially in the form of nanowires
(SiNWs), have shown great potential as pressure sensors due to their unique properties
such as high sensitivity, small size, and low power consumption. When a force is
applied to SiNWs, they undergo a mechanical deformation that results in a change in
their electrical resistance. Such an effect has been referred to as the piezoresistance
effect. This change in resistance can be measured and used to determine the amount
of pressure being applied. By integrating these nanowires into a sensor device, it is
possible to create a highly sensitive pressure sensor that can be used in a variety of
applications such as in medical devices, aerospace technology, and robotics. Many
available techniques can be applied to fabricate such SiNWs. One of the simplest ones
is the so-called metal-assisted-chemical-etching (MACE) which has gained significant
attention in recent years. This process involves the use of a metal catalyst, such as
silver, to etch silicon in a controlled manner to produce nanowires with high aspect
ratios. The nanowires can be integrated with other materials to create a flexible and
stretchable sensor that can conform to curved surfaces and be used in a variety of
applications. One advantage of using MACE to fabricate silicon nanowires is that it
is a low-cost and scalable process. This makes it possible to produce large quantities
of nanowires at a low cost, which is important for commercial applications.
This thesis describes the fabrication of SiNWs using MACE and applications of
the SiNWs as an accurate and sensitive pressure sensor for an isostatic and uniaxial
load. Its use was further extended to fabricate a novel, small, and compact, breath
sensor that could potentially have an impact on sleep research
Low Power Memory/Memristor Devices and Systems
This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within
Medical devices with embedded electronics: design and development methodology for start-ups
358 p.El sector de la biotecnología demanda innovación constante para hacer frente a los retos del sector sanitario. Hechos como la reciente pandemia COVID-19, el envejecimiento de la población, el aumento de las tasas de dependencia o la necesidad de promover la asistencia sanitaria personalizada tanto en entorno hospitalario como domiciliario, ponen de manifiesto la necesidad de desarrollar dispositivos médicos de monitorización y diagnostico cada vez más sofisticados, fiables y conectados de forma rápida y eficaz. En este escenario, los sistemas embebidos se han convertido en tecnología clave para el diseño de soluciones innovadoras de bajo coste y de forma rápida. Conscientes de la oportunidad que existe en el sector, cada vez son más las denominadas "biotech start-ups" las que se embarcan en el negocio de los dispositivos médicos. Pese a tener grandes ideas y soluciones técnicas, muchas terminan fracasando por desconocimiento del sector sanitario y de los requisitos regulatorios que se deben cumplir. La gran cantidad de requisitos técnicos y regulatorios hace que sea necesario disponer de una metodología procedimental para ejecutar dichos desarrollos. Por ello, esta tesis define y valida una metodología para el diseño y desarrollo de dispositivos médicos embebidos
- …