326,148 research outputs found
RowCore: A Processing-Near-Memory Architecture for Big Data Machine Learning
The technology-push of die stacking and application-pull of
Big Data machine learning (BDML) have created a unique
opportunity for processing-near-memory (PNM). This paper
makes four contributions: (1) While previous PNM work
explores general MapReduce workloads, we identify three
workload characteristics: (a) irregular-and-compute-light (i.e.,
perform only a few operations per input word which include
data-dependent branches and indirect memory accesses); (b)
compact (i.e., the computation has a small intermediate live
data and uses only a small amount of contiguous input data);
and (c) memory-row-dense (i.e., process the input data without
skipping over many bytes). We show that BDMLs have
or can be transformed to have these characteristics which,
except for irregularity, are necessary for bandwidth- and energyefficient
PNM, irrespective of the architecture. (2) Based on
these characteristics, we propose RowCore, a row-oriented
PNM architecture, which (pre)fetches and operates on entire
memory rows to exploit BDMLs’ row-density. Instead
of this row-centric access and compute-schedule, traditional
architectures opportunistically improve row locality while
fetching and operating on cache blocks. (3) RowCore employs
well-known MIMD execution to handle BDMLs’ irregularity,
and sequential prefetch of input data to hide memory
latency. In RowCore, however, one corelet prefetches
a row for all the corelets which may stray far from each
other due to their MIMD execution. Consequently, a leading
corelet may prematurely evict the prefetched data before
a lagging corelet has consumed the data. RowCore employs
novel cross-corelet flow-control to prevent such eviction. (4)
RowCore further exploits its flow-controlled prefetch for frequency
scaling based on novel coarse-grain compute-memory
rate-matching which decreases (increases) the processor clock
speed when the prefetch buffers are empty (full). Using simulations,
we show that RowCore improves performance and
energy, by 135% and 20% over a GPGPU with prefetch,
and by 35% and 34% over a multicore with prefetch, when
all three architectures use the same resources (i.e., number
of cores, and on-processor-die memory) and identical diestacking
(i.e., GPGPUs/multicores/RowCore and DRAM)
ETL and analysis of IoT data using OpenTSDB, Kafka, and Spark
Master's thesis in Computer scienceThe Internet of Things (IoT) is becoming increasingly prevalent in today's society. Innovations in storage and processing methodologies enable the processing of large amounts of data in a scalable manner, and generation of insights in near real-time. Data from IoT are typically time-series data but they may also have a strong spatial correlation. In addition, many time-series data are deployed in industries that still place the data in inappropriate relational databases.
Many open-source time-series databases exist today with inspiring features in terms of storage, analytic representation, and visualization. Finding an efficient method to migrate data into a time-series database is the first objective of the thesis.
In recent decades, machine learning has become one of the backbones of data innovation. With the constantly expanding amounts of information available, there is good reason to expect that smart data analysis will become more pervasive as an essential element for innovative progress. Methods for modeling time-series data in machine learning and migrating time-series data from a database to a big data machine learning framework, such as Apache Spark, is explored in this thesis
Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks
Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially more suitable (than e.g. EEG) for brain-based Human Computer Interaction (HCI). While some machine learning approaches have been used in prior HCI work, this paper explores different approaches and configurations for classifying Mental Workload (MWL) from a continuous HCI task, to identify and understand potential limitations and data processing decisions. In particular, we investigate three overall approaches: a logistic regression method, a supervised shallow method (SVM), and a supervised deep learning method (CNN). We examine personalised and gen-eralised models, as well as consider different features and ways of labelling the data. Our initial explorations show that generalised models can perform as well as personalised ones and that deep learning can be a suitable approach for medium size datasets. To provide additional practical advice for future brain-computer interaction systems, we conclude by discussing the limitations and data-preparation needs of different machine learning approaches. We also make recommendations for avenues of future work that are most promising for the machine learning of fNIRS data
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an
unprecedented amount of chest CT scans in the near future, which radiologists
will have to read in order to decide on a patient follow-up strategy. According
to the current guidelines, the workup of screen-detected nodules strongly
relies on nodule size and nodule type. In this paper, we present a deep
learning system based on multi-stream multi-scale convolutional networks, which
automatically classifies all nodule types relevant for nodule workup. The
system processes raw CT data containing a nodule without the need for any
additional information such as nodule segmentation or nodule size and learns a
representation of 3D data by analyzing an arbitrary number of 2D views of a
given nodule. The deep learning system was trained with data from the Italian
MILD screening trial and validated on an independent set of data from the
Danish DLCST screening trial. We analyze the advantage of processing nodules at
multiple scales with a multi-stream convolutional network architecture, and we
show that the proposed deep learning system achieves performance at classifying
nodule type that surpasses the one of classical machine learning approaches and
is within the inter-observer variability among four experienced human
observers.Comment: Published on Scientific Report
Machine Learning on the Cloud for Pattern Recognition
Pattern recognition is a field of machine learning with applications to areas such as text recognition and computer vision. Machine learning algorithms, such as convolutional neural networks, may be trained to classify images. However, such tasks may be computationally intensive for a commercial computer for larger volumes or larger sizes of images. Cloud computing allows one to overcome the processing and memory constraints of average commercial computers, allowing computations on larger amounts of data. In this project, we developed a system for detection and tracking of moving human and vehicle objects in videos in real time or near real time. We trained various classifiers to identify objects of interest as either vehicular or human. We then compared the accuracy of different machine learning algorithms, and we compared the training runtime between a commercial computer and a virtual machine on the cloud
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