19,066 research outputs found
Early and Accurate Modeling of Streaming Embedded Applications
This thesis presents automatic generation of fast and accurate timed models of streaming embedded applications, before the complete software-hardware platform is available. We focus on streaming applications, because they tend to be the most compute-intensive applications on mobile devices. Therefore, it is critical to optimize the hardware-software platform for streaming applications, as early as possible in the design process. As such, fast, accurate and early models are essential for hardware-software optimization.
Our design methodology is as follows. First, a measurement model is generated and executed, on the target processor, to predict the computation delays in an application. Next, the delays are annotated in the application code to generate a host-compiled model of the application. Our experiments show that such models can be generated and simulated at very high speed and accurately predict the computation load offered by the application. Our results with large streaming media applications, such as music and voice codecs, show that the estimation errors are less than 3.3%, while providing very high simulation speed. Therefore, using our models, embedded system designers can perform early optimizations to the system architecture with high confidence
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Formal and Informal Methods for Multi-Core Design Space Exploration
We propose a tool-supported methodology for design-space exploration for
embedded systems. It provides means to define high-level models of applications
and multi-processor architectures and evaluate the performance of different
deployment (mapping, scheduling) strategies while taking uncertainty into
account. We argue that this extension of the scope of formal verification is
important for the viability of the domain.Comment: In Proceedings QAPL 2014, arXiv:1406.156
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video
understanding. Having insight into the movement of pedestrians is crucial for
ensuring safe operation in a variety of applications including autonomous
vehicles, social robots, and environmental monitoring. Current works in this
area utilize complex generative or recurrent methods to capture many possible
futures. However, despite the inherent real-time nature of predicting future
paths, little work has been done to explore accurate and computationally
efficient approaches for this task. To this end, we propose a convolutional
approach for real-time pedestrian path prediction, CARPe. It utilizes a
variation of Graph Isomorphism Networks in combination with an agile
convolutional neural network design to form a fast and accurate path prediction
approach. Notable results in both inference speed and prediction accuracy are
achieved, improving FPS considerably in comparison to current state-of-the-art
methods while delivering competitive accuracy on well-known path prediction
datasets.Comment: AAAI-21 Camera Read
Development of Energy Models for Design Space Exploration of Embedded Many-Core Systems
This paper introduces a methodology to develop energy models for the design
space exploration of embedded many-core systems. The design process of such
systems can benefit from sophisticated models. Software and hardware can be
specifically optimized based on comprehensive knowledge about application
scenario and hardware behavior. The contribution of our work is an automated
framework to estimate the energy consumption at an arbitrary abstraction level
without the need to provide further information about the system. We validated
our framework with the configurable many-core system CoreVA-MPSoC. Compared to
a simulation of the CoreVA-MPSoC on gate level in a 28nm FD-SOI standard cell
technology, our framework shows an average estimation error of about 4%.Comment: Presented at HIP3ES, 201
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