Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines

Researchers have long touted a vision of the future enabled by a proliferation of internet-of-things devices, including smart sensors, homes, and cities. Increasingly, embedding intelligence in such devices involves the use of deep neural networks. However, their storage and processing requirements make them prohibitive for cheap, off-the-shelf platforms. Overcoming those requirements is necessary for enabling widely-applicable smart devices. While many ways of making models smaller and more efficient have been developed, there is a lack of understanding of which ones are best suited for particular scenarios. More importantly for edge platforms, those choices cannot be analyzed in isolation from cost and user experience. In this work, we holistically explore how quantization, model scaling, and multi-modality interact with system components such as memory, sensors, and processors. We perform this hardware/software co-design from the cost, latency, and user-experience perspective, and develop a set of guidelines for optimal system design and model deployment for the most cost-constrained platforms. We demonstrate our approach using an end-to-end, on-device, biometric user authentication system using a $20 ESP-EYE board

Efficient and Reliable Task Scheduling, Network Reprogramming, and Data Storage for Wireless Sensor Networks

Wireless sensor networks (WSNs) typically consist of a large number of resource-constrained nodes. The limited computational resources afforded by these nodes present unique development challenges. In this dissertation, we consider three such challenges. The first challenge focuses on minimizing energy usage in WSNs through intelligent duty cycling. Limited energy resources dictate the design of many embedded applications, causing such systems to be composed of small, modular tasks, scheduled periodically. In this model, each embedded device wakes, executes a task-set, and returns to sleep. These systems spend most of their time in a state of deep sleep to minimize power consumption. We refer to these systems as almost-always-sleeping (AAS) systems. We describe a series of task schedulers for AAS systems designed to maximize sleep time. We consider four scheduler designs, model their performance, and present detailed performance analysis results under varying load conditions. The second challenge focuses on a fast and reliable network reprogramming solution for WSNs based on incremental code updates. We first present VSPIN, a framework for developing incremental code update mechanisms to support efficient reprogramming of WSNs. VSPIN provides a modular testing platform on the host system to plug-in and evaluate various incremental code update algorithms. The framework supports Avrdude, among the most popular Linux-based programming tools for AVR microcontrollers. Using VSPIN, we next present an incremental code update strategy to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg\u27s Algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce the edit map size. Finally, we present experimental results exploring the reduction in data size that it enables. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions - leading to significantly lower energy costs for wireless sensor network reprogramming. The third challenge focuses on enabling fast and reliable data storage in wireless sensor systems. A file storage system that is fast, lightweight, and reliable across device failures is important to safeguard the data that these devices record. A fast and efficient file system enables sensed data to be sampled and stored quickly and batched for later transmission. A reliable file system allows seamless operation without disruptions due to hardware, software, or other unforeseen failures. While flash technology provides persistent storage by itself, it has limitations that prevent it from being used in mission-critical deployment scenarios. Hybrid memory models which utilize newer non-volatile memory technologies, such as ferroelectric RAM (FRAM), can mitigate the physical disadvantages of flash. In this vein, we present the design and implementation of LoggerFS, a fast, lightweight, and reliable file system for wireless sensor networks, which uses a hybrid memory design consisting of RAM, FRAM, and flash. LoggerFS is engineered to provide fast data storage, have a small memory footprint, and provide data reliability across system failures. LoggerFS adapts a log-structured file system approach, augmented with data persistence and reliability guarantees. A caching mechanism allows for flash wear-leveling and fast data buffering. We present a performance evaluation of LoggerFS using a prototypical in-situ sensing platform and demonstrate between 50% and 800% improvements for various workloads using the FRAM write-back cache over the implementation without the cache

Energy Efficiency of Image Transmission in Embedded Linux based Wireless Visual Sensor Network

Wireless Visual Sensor Network (WVSN) is a system that consists of visual sensor nodes with an embedded processor. WVSN devices have limited resources of energy, computation capability, memory, and bandwidth. Due to these limitations the implementation of WVSN for large multimedia data, such as images, become a challenging task. Therefore, it is required compressed images prior to transmission. In addition to the limited resources, the system implementation strongly affects the efficiency of the working system. The main contribution of this research is to offer a technical solution of simpler image compression on the WVSN platform. JPEG 2000 is investigated as an alternative compression method to reduce the size of data transfer on WVSN using Embedded Linux as its operating system. Compressed images are transferred to a receiver on communication of IEEE 802.15.4.. This paper shows that the energy consumption for compression and transmission will reduce to only 10.48%, 13.60%, and 17.11% compared to raw image. BER will significantly reduce by implementing image compression. Therefore, it is demonstrated that this model significantly increases energy efficiency, memory utilization efficiency, and data transfer time with acceptable PSNR, compared to uncompressed images

Securing Critical Infrastructures

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInoopenCarelli, Albert

Interoperating networked embedded systems to compose the web of things

Improvements in science and technology have enhanced our quality of life with better healthcare services, comfortable living and transportation among others. Human beings are now able to travel faster, communicate across the globe in fraction of seconds, understand nature better than ever before and generate and consume huge amount of information. The Internet played a central role in this development by providing a vast network of networks. Leveraging this global infrastructure, the World Wide Web is providing a shared information space for such unprecedented amount of knowledge that is mostly contributed and used by human beings. It has played such a critical role in the adoption of the Internet, it is common to find people referring specific web sites as Internet. This adoption coupled with advances in manufacturing of computing elements that led to the reduction in size and price has introduced a new wave of technology, called the Internet of Things. A rudimentary description of the Internet of Things (IoT) is an Internet that connects, not only traditional computing devices (with higher capacity and provide user interface) but also everyday physical objects or ’Things’ around us. These objects are augmented by small networked embedded computing elements that interact with the host via sensors and actuators. It is estimated that there will be Billions of such devices and Trillions of dollars of market value distributed in multiple aspects of our lives; such as healthcare, smart home, smart industries and smart cities. However, there are many challenges that are hindering the wide adoption of IoT. One of these challenges is heterogeneity of network interfaces, platforms, data formats and many standards that led to vertical islands of systems that are not interoperable at various levels. To address the lack of interoperability, this thesis presents the author’s contributions in three categories. The first part is a lightweight middleware called LISA that address variations in protocols and platforms. It is designed to work within the constrained resources of the networked embedded devices. The overhead of the middleware is evaluated and compared with other related frameworks. The second set of contributions focus on higher level of system integration and related challenges. It includes a domain specific IoT language (DoS-IL) and a server implementation to support the proposed code on demand approach. The scripting language enables re-configuration of the behaviour of systems during integration or functional changes. The related server provides abstraction of the physical object and its embedded device to provide mobility services in addition to hosting the scripts. The last set of contributions are focused on either generalized architectural style design or a specific healthcare use case. In summary, the overall thesis presents a highlevel architectural style that provides ease of understanding and communication of IoT systems, serves as a means for system level integration and provides the desired quality attributes for IoT systems. The other contributions fit in the architectural style to facilitate the adoption of the style or showcase specific instances of the architecture’s use. The performance of the middleware, the scripting language and the server including their resource utilization and overhead have been analyzed and presented. In general, the combination of the contributions enable inter-operation of networked embedded systems that serve as building blocks for the Web of Things - a global system of IoT systems

Embedded Electronics In Medical Applications

Proceedings of"Conference on Recent Advances in Biomaterials Dec 17-18 '10"Held at Saveetha School of Engineering, Saveetha University, Thandalam, Chennai-602 105, Tamilnadu, IndiaTheme 10Embedded Electronics In Medical Application

A fast and accurate energy source emulator for wireless sensor networks

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