Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the traffic speed prediction as a case study, a vanilla LSTM model with the optimised LSTM cell achieves 17534 inferences per second while consuming only 3.8 $\mu$J per inference on the FPGA XC7S15 from Spartan-7 family. It achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy efficient than existing approaches.Comment: 12 pages, 7 figure

On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data

Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception. Instead of deploying soft sensors on the Cloud, this study shift towards employing on-device soft sensors, promising heightened efficiency and bolstering data security. Our approach substantially improves energy efficiency by deploying Artificial Intelligence (AI) directly on devices within a wireless sensor network. Furthermore, the synergistic integration of the Microcontroller Unit and Field-Programmable Gate Array (FPGA) leverages the rapid AI inference capabilities of the latter. Empirical evidence from our real-world use case demonstrates that FPGA-based soft sensors achieve inference times ranging remarkably from 1.04 to 12.04 microseconds. These compelling results highlight the considerable potential of our innovative approach for executing real-time inference tasks efficiently, thereby presenting a feasible alternative that effectively addresses the latency challenges intrinsic to Cloud-based deployments.Comment: 8 pages, 6 figures, 1 Table, Accepted by the 1st AUTONOMOUS UBIQUITOUS SYSTEMS (AUTOQUITOUS) WORKSHOP of EAI MobiQuitous 2023 - 20th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Service

Traceable Use of Emerging Technologies in Smart Systems

This volume presents a selection of invited papers from the 3rd Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2022): From Data to Information and Knowledge, held in Yerevan, Armenia, August, 23-25, and further articles from a free call for papers JUCS-CODASSCA-2023 published by Easychair. The workshop continues the cooperation between the University of Duisburg‐Essen (UDE) and the American University of Armenia (AUA) funded by the German Academic Exchange Service (DAAD) and the German Research Foundation (DFG). The workshop took place together with a one-week summer school on the topic Enhancements of Deep Learning for Intelligent Applications and the Connected Society.&nbsp

Towards Interoperable IoT Deployments inSmart Cities - How project VITAL enables smart, secure and cost- effective cities

International audienceIoT-based deployments in smart cities raise several challenges, especially in terms of interoperability. In this paper, we illustrate semantic interoperability solutions for IoT systems. Based on these solutions, we describe how the FP7 VITAL project aims to bridge numerous silo IoT deployments in smart cities through repurposing and reusing sensors and data streams across multiple applications without carelessly compromising citizens’ security and privacy. This approach holds the promise of increasing the Return-On-Investment (ROI), which is associated with the usually costly smart city infrastructures, through expanding the number and scope of potential applications

Vision: a Lightweight Computing Model for Fine-Grained Cloud Computing

Cloud systems differ fundamentally in how they offer and charge for resources. While some systems provide a generic programming abstraction at coarse granularity, e.g., a virtual machine rented by the hour, others offer specialized abstractions with fine-grained accounting on a per-request basis. In this paper, we explore Tasklets, an abstraction for instances of short-duration, generic computations that migrate from a host requiring computation to hosts that are willing to provide computation. Tasklets enable fine-grained accounting of resource usage, enabling us to build infrastructure that supports trading computing resources according to various economic models. This computation model is especially attractive in settings where mobile devices can utilize resources in the cloud to mitigate local resource constraints

A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Integrating Wireless Sensor Networks within a City Cloud

International audienceSmart City solutions are currently based on multiple architectures, standards and platforms, which have led to a highly fragmented landscape. In order to allow cities to share data across systems and coordinate processes across domains, it is essential to break these silos. A way to achieve the purpose is sensor virtualization, discovery and data restitution. In this paper, a federation of FIT IoT-LAB within OpenIoT is presented. OpenIoT is a middleware that enables the collection of data streams from multiple heterogeneous geographically dispersed data sources, as well as their semantic unification and streaming with a cloud infrastructure. Future Internet of Things IoT-LAB (FIT IoT-LAB) provides a very large scale infrastructure facility suitable for testing small wireless sensor devices and heterogeneous communicating objects. The integration proposed represents a way to reduce the gap existing in the IoT fragmentation, and, moreover, allows users to develop smart city applications by interacting directly with sensors at different layers. We illustrate it trough a basic temperature monitoring application to show its efficiency

FPGA based in-memory AI computing

The advent of AI in vehicles of all kinds is simultaneously creating the need for more and most often also very large computing capacities. Depending on the type of vehicle, this gives rise to various problems: while overall hardware and engineering costs dominate for airplanes, in fully electrical cars the costs for computing hardware are more of a matter. Common in both domains are tight requirements on the size, weight and space of the hardware, especially for drones and satellites, where this is most challenging. For airplanes and especially for satellites, an additional challenge is the radiation resistance of the usually very memory-intensive AI systems. We therefore propose an FPGA-based in-memory AI computation methodology, which is so far only applicable for small AI systems, but works exclusively with the local memory elements of FPGAs: lookup tables (LUTs) and registers. By not using external and thus slow, inefficient and radiation-sensitive DRAM, but only local SRAM, we can make AI systems faster, lighter and more efficient than is possible with conventional GPUs or AI accelerators. All known radiation hardening techniques for FPGAs also work for our systems

Dynamic optical coherence tomography of blood vessels in cutaneous melanoma — correlation with histology, immunohistochemistry and dermoscopy

Dermoscopy adds important information to the assessment of cutaneous melanoma, but the risk of progression is predicted by histologic parameters and therefore requires surgery and histopathologic preparation. Neo-vascularization is crucial for tumor progression and worsens prognosis. The aim of this study was the in vivo evaluation of blood vessel patterns in melanoma with dynamic optical coherence tomography (D-OCT) and the correlation with dermoscopic and histologic malignancy parameters for the risk assessment of melanoma. In D-OCT vessel patterns, shape, distribution and presence/type of branching of 49 melanomas were evaluated in vivo at three depths and correlated with the same patterns in dermoscopy and with histologic parameters after excision. In D-OCT, blood vessel density and atypical shapes (coils and serpiginous vessels) increased with higher tumor stage. The histologic parameters ulceration and Hmb45- and Ki67-positivity increased, whereas regression, inflammation and PD-L1-positivity decreased with risk. CD31, VEGF and Podoplanin correlated with D-OCT vasculature findings. B-RAF mutation status had no influence. Due to pigment overlay and the summation effect, the vessel evaluation in dermoscopy and D-OCT did not correlate well. In summary, atypical vessel patterns in melanoma correlate with histologic parameters for risk for metastases. Tumor vasculature can be noninvasively assessed using D-OCT before surgery