With the emergence of edge-computing platforms, the applications of smart wearable devices are immense. This technology can be incorporated in consumer products such as smartwatches and activity trackers, for continuo...
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With the emergence of edge-computing platforms, the applications of smart wearable devices are immense. This technology can be incorporated in consumer products such as smartwatches and activity trackers, for continuous health monitoring, as well as for medical applications such as myoelectric prosthetics, to interpret the electric activity in the residual limb and achieve fast and precise control. However, wearable technologies require a lightweight, energy-efficient, and low-latency processing system in order to extend the devices' autonomy while maintaining a realistic user-feedback interaction. Neuromorphic processing, thanks to its event-based and asynchronous nature, presents ideal characteristics for compact brain-inspired low-power and ultra-fast computing systems that can enable a new generation of wearable devices. This article presents two spiking neural networks (SNNs) for event-based electromyography (EMG) gesture recognition and their evaluation on Intel's research neuromorphic chip Loihi. Specifically, the evaluation is done on the Kapoho Bay platform which embeds the Loihi processor in a Universal Serial Bus (USB) form factor device allowing for closed-loop edge computation. With accurate experimental evaluation, this article demonstrates that the proposed method is able to discriminate 12 different hand gestures using an eight-channel EMG sensor and exceeds state-of-the-art results. We obtained an accuracy of 74% on the commonly used NinaPro DB5 dataset, a processing latency of 5.7 ms for 300-ms EMG samples while consuming only 41 mW.
Using Software-Defined Networking (SDN), the flexibility and programmability of networks can be significantly increased through the decoupling of the control and data planes. However, network scale-up in large-scale d...
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Using Software-Defined Networking (SDN), the flexibility and programmability of networks can be significantly increased through the decoupling of the control and data planes. However, network scale-up in large-scale data centers can rapidly increase the computational complexity of operations such as the shortest path calculation on the network topology or Quality-of-Service (QoS) routing, which, in turn, can cause scalability problems in current SDN controllers. This paper proposes ParaFlow, a multithreaded SDN controller that supports fine-grained parallelism by exploiting application parallelism and utilizing multi-/many-core resources to accelerate event processing. ParaFlow also provides a flow-basedprogramming interface that allows application developers to program with network flows rather than various types of low-level events. Experimental results show that ParaFlow achieves satisfactory performance and scalability in the multithreaded case.
JavaScript, the most popular language on the Web, is rapidly moving to the server-side, becoming even more pervasive. Still, JavaScript lacks support for shared memory parallelism, making it challenging for developers...
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ISBN:
(纸本)9781450319225
JavaScript, the most popular language on the Web, is rapidly moving to the server-side, becoming even more pervasive. Still, JavaScript lacks support for shared memory parallelism, making it challenging for developers to exploit multicores present in both servers and clients. In this paper we present TigerQuoll, a novel API and runtime for parallel programming in JavaScript. TigerQuoll features an event-based API and a parallel runtime allowing applications to exploit a mutable shared memory space. The programming model of TigerQuoll features automatic consistency and concurrency management, such that developers do not have to deal with shared-data synchronization. TigerQuoll supports an innovative transaction model that allows for eventual consistency to speed up high-contention workloads. Experiments show that TigerQuoll applications scale well, allowing one to implement common parallelism patterns in JavaScript.
Recent trends in programming models for server-side development have shown an increasing popularity of event-based single- threaded programming models based on the combination of dynamic languages such as JavaScript a...
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Recent trends in programming models for server-side development have shown an increasing popularity of event-based single- threaded programming models based on the combination of dynamic languages such as JavaScript and event-based runtime systems for asynchronous I/O management such as ***. Reasons for the success of such models are the simplicity of the single-threaded event-based programming model as well as the growing popularity of the Cloud as a deployment platform for Web applications. Unfortunately, the popularity of single-threaded models comes at the price of performance and scalability, as single-threaded event-based models present limitations when parallel processing is needed, and traditional approaches to concurrency such as threads and locks don''t play well with event-based systems. This dissertation proposes a programming model and a runtime system to overcome such limitations by enabling single-threaded event-based applications with support for
speculative parallel execution. The model, called Parallel event Loop, has the goal of bringing parallel execution to the domain of single-threaded event-based programming without relaxing the main characteristics of the single-threaded model, and therefore providing developers with the impression of a safe, single-threaded, runtime. Rather than supporting only pure single-threaded programming, however, the parallel event loop can also be used to derive safe, high-level, parallel programming models characterized by a strong compatibility with single-threaded runtimes. We describe three distinct implementations of speculative runtimes enabling the parallel execution of event-based applications. The first implementation we describe is a pessimistic runtime system based on locks to implement speculative parallelization. The second and the third implementations are based on two distinct optimistic runtimes using software transactional memory. Each of the implementations supports the
parallelization of applic
Computational Thinking Through Mobile Computing is an NSF-funded project for introducing students to computational thinking through creating mobile apps. In this hands-on workshop, which is targeted at undergraduate a...
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ISBN:
(纸本)9781450326056
Computational Thinking Through Mobile Computing is an NSF-funded project for introducing students to computational thinking through creating mobile apps. In this hands-on workshop, which is targeted at undergraduate and secondary school computer science teachers, participants will develop Android apps using MIT App Inventor 2. This is a new version of the visual blocks-basedprogramming environment with additional language features (e.g., local variables) and browser-based blocks editing. The workshop will also present pedagogical materials (lessons, tutorials, assignments), evaluation materials (blocks-based quizzes, surveys, project rubrics), and student projects. All of the pedagogical materials presented in the workshop, as well as all of the materials used by the workshop presenters in their individual courses, are posted on the Web and are available to everyone under a Creative Commons license. A laptop is required for this workshop. Each participant will be provided with an Android mobile device to use during the workshop. Participants who have their own Android phones or tablets can use them if they choose. This workshop is based upon work supported by the National Science Foundation under Grant Numbers 1225680, 1225719, 1225745, 1225976, and 1226216.
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