859 research outputs found
Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin
Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA
Decision trees are machine learning models commonly used in various
application scenarios. In the era of big data, traditional decision tree
induction algorithms are not suitable for learning large-scale datasets due to
their stringent data storage requirement. Online decision tree learning
algorithms have been devised to tackle this problem by concurrently training
with incoming samples and providing inference results. However, even the most
up-to-date online tree learning algorithms still suffer from either high memory
usage or high computational intensity with dependency and long latency, making
them challenging to implement in hardware. To overcome these difficulties, we
introduce a new quantile-based algorithm to improve the induction of the
Hoeffding tree, one of the state-of-the-art online learning models. The
proposed algorithm is light-weight in terms of both memory and computational
demand, while still maintaining high generalization ability. A series of
optimization techniques dedicated to the proposed algorithm have been
investigated from the hardware perspective, including coarse-grained and
fine-grained parallelism, dynamic and memory-based resource sharing, pipelining
with data forwarding. We further present a high-performance, hardware-efficient
and scalable online decision tree learning system on a field-programmable gate
array (FPGA) with system-level optimization techniques. Experimental results
show that our proposed algorithm outperforms the state-of-the-art Hoeffding
tree learning method, leading to 0.05% to 12.3% improvement in inference
accuracy. Real implementation of the complete learning system on the FPGA
demonstrates a 384x to 1581x speedup in execution time over the
state-of-the-art design.Comment: appear as a conference paper in FCCM 201
Sherlock : a Semi-Automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure
Acknowledgments This work is supported by the BBC Connected Studio programme (http://www.bbc.co.uk/partnersandsuppliers/con nectedstudio/), the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub; award reference EP/G066051/1, the award made by UK Economic & Social Research Council (ESRC); award reference ES/M001628/1, National Natural Science Foundation of China (NSFC) under Grant No. 61373051, and the China National Science and Technology Pillar Program (Grant No. 2013BAH07F05). The authors would like to thank Ryan Hussey for the work on the user interface design and Tom Cass and James Ruston for the help in developing the Sherlock application. We are also grateful to Herm Baskerville for creating the editorial quizzes and Nava Tintarev for many helpful discussions on the human evaluation.Peer reviewedPublisher PD
The Research of Reverse-Time Migration for Cross-Hole Seismic
Cross-hole seismic is the leading technology of development seismology, which is still developing and improving. With the development of down-hole acquisition equipment, cross-hole seismic acquisition technology is getting mature, providing better data for imaging. According to the features of cross-hole survey and cross-hole data, we put forward a reverse-time migration method which is suitable for the wave equation for cross-hole seismic data. We propose finite difference scheme of higher order, and then derive its stability condition in cross-hole seismic. The frequency dispersion problem in cross-hole seismic wave field extrapolation is also discussed. Cross correlation imaging condition is used to realize migration, and Laplace filter is applied to remove low-frequency noise from migration section. Thus finite-difference reverse-time migration method for cross-hole seismic is established. Finally, we build geological models with anomalous ellipsoids, and apply cross-hole seismic wave field simulation and migration to them, thus our method proves its effectiveness. When dealing with real cross-hole seismic data with this method, high-resolution migration sections can be achieved.Key words: Cross-hole seismic; Reverse-time migration; Model tes
HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis
High-level synthesis (HLS) enables designers to customize hardware designs
efficiently. However, it is still challenging to foresee the correlation
between power consumption and HLS-based applications at an early design stage.
To overcome this problem, we introduce HL-Pow, a power modeling framework for
FPGA HLS based on state-of-the-art machine learning techniques. HL-Pow
incorporates an automated feature construction flow to efficiently identify and
extract features that exert a major influence on power consumption, simply
based upon HLS results, and a modeling flow that can build an accurate and
generic power model applicable to a variety of designs with HLS. By using
HL-Pow, the power evaluation process for FPGA designs can be significantly
expedited because the power inference of HL-Pow is established on HLS instead
of the time-consuming register-transfer level (RTL) implementation flow.
Experimental results demonstrate that HL-Pow can achieve accurate power
modeling that is only 4.67% (24.02 mW) away from onboard power measurement. To
further facilitate power-oriented optimizations, we describe a novel design
space exploration (DSE) algorithm built on top of HL-Pow to trade off between
latency and power consumption. This algorithm can reach a close approximation
of the real Pareto frontier while only requiring running HLS flow for 20% of
design points in the entire design space.Comment: published as a conference paper in ASP-DAC 202
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