24,903 research outputs found
Towards a Distributed Quantum Computing Ecosystem
The Quantum Internet, by enabling quantum communications among remote quantum
nodes, is a network capable of supporting functionalities with no direct
counterpart in the classical world. Indeed, with the network and communications
functionalities provided by the Quantum Internet, remote quantum devices can
communicate and cooperate for solving challenging computational tasks by
adopting a distributed computing approach. The aim of this paper is to provide
the reader with an overview about the main challenges and open problems arising
with the design of a Distributed Quantum Computing ecosystem. For this, we
provide a survey, following a bottom-up approach, from a communications
engineering perspective. We start by introducing the Quantum Internet as the
fundamental underlying infrastructure of the Distributed Quantum Computing
ecosystem. Then we go further, by elaborating on a high-level system
abstraction of the Distributed Quantum Computing ecosystem. Such an abstraction
is described through a set of logical layers. Thereby, we clarify dependencies
among the aforementioned layers and, at the same time, a road-map emerges
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Technical Review of Residential Programmable Communicating Thermostat Implementation for Title 24-2008
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Distributed Feature Extraction Using Cloud Computing Resources
The need to expand the computational resources in a massive surveillance network is clear but traditional means of purchasing new equipment for short-term tasks every year is wasteful. In this work I will provide evidence in support of utilizing a cloud computing infrastructure to perform computationally intensive feature extraction tasks on data streams. Efficient off-loading of computational tasks to cloud resources will require a minimization of the time needed to expand the cloud resources, an efficient model of communication and a study of the interplay between the in-network computational resources and remote resources in the cloud. This report provides strong evidence that the use of cloud computing resources in a near real-time distributed sensor network surveillance system, ASAP, is feasible. A face detection web service operating on an Amazon EC2 instance is shown to provide processing of 10-15 frames per second.Umakishore Ramachandran - Faculty Mentor ; Rajnish Kumar - Committee Member/Second Reade
Ianus: an Adpative FPGA Computer
Dedicated machines designed for specific computational algorithms can
outperform conventional computers by several orders of magnitude. In this note
we describe {\it Ianus}, a new generation FPGA based machine and its basic
features: hardware integration and wide reprogrammability. Our goal is to build
a machine that can fully exploit the performance potential of new generation
FPGA devices. We also plan a software platform which simplifies its
programming, in order to extend its intended range of application to a wide
class of interesting and computationally demanding problems. The decision to
develop a dedicated processor is a complex one, involving careful assessment of
its performance lead, during its expected lifetime, over traditional computers,
taking into account their performance increase, as predicted by Moore's law. We
discuss this point in detail
Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets
Both the scientific community and the popular press have paid much attention
to the speed of the Securities Information Processor, the data feed
consolidating all trades and quotes across the US stock market. Rather than the
speed of the Securities Information Processor, or SIP, we focus here on its
accuracy. Relying on Trade and Quote data, we provide various measures of SIP
latency relative to high-speed data feeds between exchanges, known as direct
feeds. We use first differences to highlight not only the divergence between
the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP.
We find that as many as 60 percent or more of trades are reported out of
sequence for stocks with high trade volume, therefore skewing simple measures
such as returns. While not yet definitive, this analysis supports our
preliminary conclusion that the underlying infrastructure of the SIP is
currently unable to keep pace with the trading activity in today's stock
market.Comment: 18 pages, 20 figures, 2 table
Constructing cluster of simple FPGA boards for cryptologic computations
In this paper, we propose an FPGA cluster infrastructure, which can be utilized in implementing cryptanalytic attacks and accelerating cryptographic operations. The cluster can be formed using simple and inexpensive, off-the-shelf FPGA boards featuring an FPGA device, local storage, CPLD, and network connection. Forming the cluster is simple and no effort for the hardware development is needed except for the hardware design for the actual computation. Using a softcore processor on FPGA, we are able to configure FPGA devices dynamically and change their configuration on the fly from a remote computer. The softcore on FPGA can execute relatively complicated programs for mundane tasks unworthy of FPGA resources. Finally, we propose and implement a fast and efficient dynamic configuration switch technique that is shown to be useful especially in cryptanalytic applications. Our infrastructure provides a cost-effective alternative for formerly proposed cryptanalytic engines based on FPGA devices
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