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Always ON always sensing small form factor edge systems for internet of things (IOT) are becoming ubiquitous. Many applications require these tiny devices to be self-powered and maintenance-free. Hence they should be able to harvest energy from available ambient sources and should have low manufacturing cost. Millimeter-scale form factor systems have been developed in academia for the past few years. Small form factor edge systems are becoming commercially available. These systems are essential in today’s cyber physical world. We will introduce the available market and the trends driving this growth in IOT system deployments. That will be followed by typical system requirements for a typical self-powered IOT system. Challenges to realize such a dream IOT system will be discussed. We will present two approaches to system design, namely bottom-up and top-down. An X86-based tiny microcontroller unit (MCU) was designed to enable multiple IOT usages. This MCU followed a bottom-up approach – ultra-low power low cost MCU was designed first and then applied to IOT systems such as smart sensor tag for package tracking. The discussion will introduce another IOT system that followed a top-down usage-driven approach. In this case, an agricultural usage was chosen that required energy harvesting, X86-class edge computing, visual recognition on the edge, secure storage, secure wireless communication and ultra-low power maintenance free operation. An IOT system was architected for this usage and later demonstrated. We will conclude the presentation with comparison of these two distinct approaches to IOT system design.
10:30 | Understanding the sources of power consumption in Mobile SoCs ABSTRACT. Very deep scaling and the poor cooling methods in mobile devices are making the leakage power of a great concern. In this paper and unlike previous works in which both dynamic and leakage power analysis was carried out only at the cluster level (coarse grain), we analyze the leakage power problem of mobile SoCs at the core level, by proposing for the first time a fine-grain leakage and dynamic power identification for each SoC unit taking the advantage of the Blind Power Identification (BPI) technique after introducing new improvements to increase its accuracy. We also introduce a new experimental methodology to apply the BPI technique for heterogeneous systems, including a novel initialization for the algorithm that enhances the output accuracy. By using some benchmarks on an octa-core Snapdragon 835 processor we show that the the total idle power is between 28 mW - 953 mW for the big cluster and between 42 mW to 865 mWfor the LTTLE cores. We show as well the different trade-offs between power consumption and performance while running a workload as single threaded compared to multi-threaded at different frequencies. Finally, we shed light on the power usage of "angry-birds" mobile game as a real life example, showing how power is divided in that case between big and LITTLE cores; and the GPU. The numbers show in this case that the online cores consume 61.5% of the total power, while the idle power of the offline cores was 38.5% showing the significance of the idle power in mobile platforms. Beside the proposed technique, this work gives useful numbers, that give an insight about the power consumption at a fine grain level, which can be of great help to determine the available thermal and power headroom to improve furthermore the overall performance of mobile devices. |
11:00 | Appliances identification for different electrical signatures using moving average as data preparation SPEAKER: Innocent Mpawenimana ABSTRACT. Abstract— Intelligent electronic equipment and automation network are the brain of high-technology energy management systems in the critical role of smart homes dominance. The smart home is a technology integration for greater comfort, autonomy, reduced cost as well as energy saving. In this paper, a system which can automatically recognize home appliances and based on a dataset of electric consumption profiles is proposed. The dataset ACS-F1 (Appliance Consumption Signature Fribourg 1) available online and containing 100 appliances signatures in XML (Extensible Markup Language) format is used for that purpose. A new format for this dataset is created as it makes easier to implement directly machine learning algorithm such as K-NN (K-Nearest Neighbors), Random Forest and Multilayer Perceptron in the feature space between the test object and the training examples. In order to optimize the classification algorithm accuracy, we propose to use a moving average function for reducing the random variations in the observations. Using this technique indeed allows the structure of the underlying causal processes to be better exposed. Moving average is widely used in trading algorithm to predict the future price movements based on identifying patterns in prices, volume and other market statistics. Recognition results using K-NN based machine learning are provided to show the impact of the number and the type of electrical signatures. In the best case an accuracy rate of 89.1% and 99.1% is obtained using K-NN, without and with moving average respectively. Our approach is compared with another data preparation technique based on dynamical coefficient and used to optimize the K-NN classifier as well. Finally, our approach based on moving average is also evaluated with Random Forest (99%) and Multilayer Perceptron (98.8%) classification algorithms for the best electrical signature obtained with K-NN |
11:30 | Secure Application Continuity in Intermittent Systems ABSTRACT. Intermittent systems operate embedded devices without a source of constant reliable power, relying instead on an unreliable source such as an energy harvester. They overcome the limitation of intermittent power by retaining and restoring system state as checkpoints across periods of power loss. Previous works have addressed a multitude of problems created by the intermittent paradigm, but do not consider securing intermittent systems. In this paper, we address the security concerns created through the introduction of checkpoints to an embedded device. When the non-volatile memory that holds checkpoints can be tampered, the checkpoints can be replayed or duplicated. We propose secure application continuity as a defense against these attacks. Secure application continuity provides assurance that an application continues where it left off upon power loss. In our secure continuity solution, we define a protocol that adds integrity, authenticity, and freshness to create secure checkpoints. We develop two solutions for our secure checkpointing design. The first solution uses a hardware accelerated implementation of AES, while the second one is based on a software implementation of a lightweight cryptographic algorithm, Chaskey. We analyze the feasibility and overhead of these designs in terms of energy consumption, execution time, and code size across several application configurations. Then, we compare this overhead to a non-secure checkpointing system similar to QuickRecall. We conclude that securing application continuity does not come cheap and that it increases the overhead of checkpoint restoration from 3.79 µJ to 42.96 µJ with the hardware accelerated solution and 57.02 µJ with the software based solution. To our knowledge, no one has yet considered the cost to provide security guarantees for intermittent operations. Our work provides future developers with an empirical evaluation of this cost, and with a problem statement for future research in this area. |
13:30 | Performance and Energy Evaluation of SAR Reconstruction on Intel Knights Landing ABSTRACT. The reconstruction of nxn-pixel Synthetic Aperture Radar (SAR) imagery using a Back Projection algorithm incurs O(n^2.m) cost, where n^2 is the number of pixels and m is the number of pulses. We have developd parallel algorithms and software for constructing multi-resolution SAR images for many-core architectures. We also develop load balancing algorithms for distributing workload to the available cores, thereby optimizing performance and energy. We evaluate the performance of our algorithms and the resulting energy consumption on an Intel Knights Landing (KNL) processor. We also present a comparison of runtime and energy between KNL, Ivy Bridge and Tesla K40m. |
14:00 | Near Data Filtering for Distributed Database Systems ABSTRACT. Over the past decade, data movement costs dominate the execution time of data-intensive applications for distributed systems and they are expected to be even more important in the future. Near data processing is a straightforward solution to reduce data movement which brings compute resources closer to the data source. This paper explores near data processing in a generic distributed system to improve the performance by reducing data movement. An efficient near data filtering solution is designed and implemented by introducing a filter layer which performs tuple-level near data filtering. In order to reduce idle time of processing nodes and improve data transmission throughput the proposed solution is extended to support block-level near data filtering by creating index for each data block. Furthermore, to answer the question when and how to perform near data filtering this paper proposes an adaptive near data filtering solution to balance the computation and data transmission throughput. Experimental results show that the proposed solutions are superior to the best existing method for most cases. The adaptive near data filtering solution achieves an average speedup factor of 4.59 for queries with low selectivity. |
14:30 | A Self-Sustaining Micro-Watt Programmable Smart Audio Sensor for Always-On Sensing SPEAKER: Michele Magno ABSTRACT. Self-sustainable always-on sensors are crucial for the Internet of Things and its emerging applications. However, achieving perpetual work with active sensors poses many challenges, especially in ultra-low power design and micro-power energy harvesting that can supply the sensors. This paper presents a smart sensor that combines energy harvesting and a micro-power event-driven sensor to achieve a self-sustaining programmable smart microphone for acoustic monitoring. The proposed solution is able to achieve programmable pattern recognition with up to 128 simultaneous time-frequency features exploiting mixed-signal low power design. Experimental results show that the designed circuit consumes only 26.89 µW in always-on mode, during the time-frequency feature-extraction, while the whole system consumes only 63 µW during pattern recognition including the power for a commercial MEMS microphone and the energy harvesting subsystem. We demonstrate that the sensor can operate perpetually powered with a small form factor flexible photovoltaic panel in indoor lighting conditions. Finally, with in-field experiments with two different audio streams the smart sensors achieved a high accuracy in the detection of 100%. |
Moderator: Adam Hahn; Panelists:
- Shrirang Abhyankar (Argonne National Laboratory) - HELICS: An open-source transmission-distribution-communication co-simulation platform to assess impacts of large penetration of distributed energy resources
- Martin Burns, (National Institute of Standards and Technology) - Transactive energy challenges and simulation-based abstract components models
- Srinivas Katipamula (Pacific Northwest National Laboratory) - Increasing building energy efficiency through market based transactive control
- Katrina Kelly (University of Pittsburg) - IoT and Sustainability: Using data to quantify and define Community Resilience
Many-core processing platforms are gaining significant interest for a wide range of applications, viz., Internet of Things (IoT), consumer electronics, single-chip cloud computers, supercomputers, defense applications etc. With billions of physical devices interconnected to each other communicating continuously, huge amount of data is expected to be transferred, stored, analyzed and computed. Data centers and servers involved are equipped with many-core processing units which analyze the data, perform arithmetic and logical operations on them, and take decisions based on the results for multiple applications. Since the applications are quite diverse, the demand on compute platform will vary significantly. As it is not feasible to have customized solutions for all different applications, a platform that can be easily modified to suit particular application demands along with optimal power efficiency and robust hardware will be highly desirable. A platform based solution that is optimal, reliable and power-aware to sustain and provide scalability to this trend is proposed in this special session.