65 research outputs found
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
Convolution is a fundamental operation in many applications, such as computer
vision, natural language processing, image processing, etc. Recent successes of
convolutional neural networks in various deep learning applications put even
higher demand on fast convolution. The high computation throughput and memory
bandwidth of graphics processing units (GPUs) make GPUs a natural choice for
accelerating convolution operations. However, maximally exploiting the
available memory bandwidth of GPUs for convolution is a challenging task. This
paper introduces a general model to address the mismatch between the memory
bank width of GPUs and computation data width of threads. Based on this model,
we develop two convolution kernels, one for the general case and the other for
a special case with one input channel. By carefully optimizing memory access
patterns and computation patterns, we design a communication-optimized kernel
for the special case and a communication-reduced kernel for the general case.
Experimental data based on implementations on Kepler GPUs show that our kernels
achieve 5.16X and 35.5% average performance improvement over the latest cuDNN
library, for the special case and the general case, respectively
Neuron Segmentation Using Deep Complete Bipartite Networks
In this paper, we consider the problem of automatically segmenting neuronal
cells in dual-color confocal microscopy images. This problem is a key task in
various quantitative analysis applications in neuroscience, such as tracing
cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using
fully convolutional networks (FCN), has profoundly changed segmentation
research in biomedical imaging. We face two major challenges in this problem.
First, neuronal cells may form dense clusters, making it difficult to correctly
identify all individual cells (even to human experts). Consequently,
segmentation results of the known FCN-type models are not accurate enough.
Second, pixel-wise ground truth is difficult to obtain. Only a limited amount
of approximate instance-wise annotation can be collected, which makes the
training of FCN models quite cumbersome. We propose a new FCN-type deep
learning model, called deep complete bipartite networks (CB-Net), and a new
scheme for leveraging approximate instance-wise annotation to train our
pixel-wise prediction model. Evaluated using seven real datasets, our proposed
new CB-Net model outperforms the state-of-the-art FCN models and produces
neuron segmentation results of remarkable qualityComment: miccai 201
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold
Inspection robots are widely used in the field of smart grid monitoring in
substations, and partial discharge (PD) is an important sign of the insulation
state of equipments. PD direction of arrival (DOA) algorithms using
conventional beamforming and time difference of arrival (TDOA) require
large-scale antenna arrays and high computational complexity, which make them
difficult to implement on inspection robots. To address this problem, a novel
directional multiple signal classification (Dir-MUSIC) algorithm for PD
direction finding based on signal strength is proposed, and a miniaturized
directional spiral antenna circular array is designed in this paper. First, the
Dir-MUSIC algorithm is derived based on the array manifold characteristics.
This method uses strength intensity information rather than the TDOA
information, which could reduce the computational difficulty and the
requirement of array size. Second, the effects of signal-to-noise ratio (SNR)
and array manifold error on the performance of the algorithm are discussed
through simulations in detail. Then according to the positioning requirements,
the antenna array and its arrangement are developed, optimized, and simulation
results suggested that the algorithm has reliable direction-finding performance
in the form of 6 elements. Finally, the effectiveness of the algorithm is
tested by using the designed spiral circular array in real scenarios. The
experimental results show that the PD direction-finding error is 3.39{\deg},
which can meet the need for Partial discharge DOA estimation using inspection
robots in substations.Comment: 8 pages,13 figures,24 reference
EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation
Artificial intelligence (AI) has been widely used in bioimage image analysis
nowadays, but the efficiency of AI models, like the energy consumption and
latency is not ignorable due to the growing model size and complexity, as well
as the fast-growing analysis needs in modern biomedical studies. Like we can
compress large images for efficient storage and sharing, we can also compress
the AI models for efficient applications and deployment. In this work, we
present EfficientBioAI, a plug-and-play toolbox that can compress given
bioimaging AI models for them to run with significantly reduced energy cost and
inference time on both CPU and GPU, without compromise on accuracy. In some
cases, the prediction accuracy could even increase after compression, since the
compression procedure could remove redundant information in the model
representation and therefore reduce over-fitting. From four different bioimage
analysis applications, we observed around 2-5 times speed-up during inference
and 30-80 saving in energy. Cutting the runtime of large scale bioimage
analysis from days to hours or getting a two-minutes bioimaging AI model
inference done in near real-time will open new doors for method development and
biomedical discoveries. We hope our toolbox will facilitate
resource-constrained bioimaging AI and accelerate large-scale AI-based
quantitative biological studies in an eco-friendly way, as well as stimulate
further research on the efficiency of bioimaging AI.Comment: 17 pages, 6 figure
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Abstract Segmentation of 3D images is a fundamental problem in biomedical image analysis. Deep learning (DL) approaches have achieved state-of-the-art segmentation performance. To exploit the 3D contexts using neural networks, known DL segmentation methods, including 3D convolution, 2D convolution on planes orthogonal to 2D image slices, and LSTM in multiple directions, all suffer incompatibility with the highly anisotropic dimensions in common 3D biomedical images. In this paper, we propose a new DL framework for 3D image segmentation, based on a combination of a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively. To our best knowledge, this is the first DL framework for 3D image segmentation that explicitly leverages 3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal Structure Segmentation Challenge and in-house image stacks for 3D fungus segmentation, our approach achieves promising results comparing to the known DL-based 3D segmentation approaches
Development of a Non-invasive Deep Brain Stimulator With Precise Positioning and Real-Time Monitoring of Bioimpedance
Methods by which to achieve non-invasive deep brain stimulation via temporally interfering with electric fields have been proposed, but the precision of the positioning of the stimulation and the reliability and stability of the outputs require improvement. In this study, a temporally interfering electrical stimulator was developed based on a neuromodulation technique using the interference modulation waveform produced by several high-frequency electrical stimuli to treat neurodegenerative diseases. The device and auxiliary software constitute a non-invasive neuromodulation system. The technical problems related to the multichannel high-precision output of the device were solved by an analog phase accumulator and a special driving circuit to reduce crosstalk. The function of measuring bioimpedance in real time was integrated into the stimulator to improve effectiveness. Finite element simulation and phantom measurements were performed to find the functional relations among the target coordinates, current ratio, and electrode position in the simplified model. Then, an appropriate approach was proposed to find electrode configurations for desired target locations in a detailed and realistic mouse model. A mouse validation experiment was carried out under the guidance of a simulation, and the reliability and positioning accuracy of temporally interfering electric stimulators were verified. Stimulator improvement and precision positioning solutions promise opportunities for further studies of temporally interfering electrical stimulation
Cathelicidin-BF, a Snake Cathelicidin-Derived Antimicrobial Peptide, Could Be an Excellent Therapeutic Agent for Acne Vulgaris
Cathelicidins are a family of antimicrobial peptides acting as multifunctional effector molecules in innate immunity. Cathelicidin-BF has been purified from the snake venoms of Bungarus fasciatus and it is the first identified cathelicidin antimicrobial peptide in reptiles. In this study, cathelicidin-BF was found exerting strong antibacterial activities against Propionibacterium acnes. Its minimal inhibitory concentration against two strains of P. acnes was 4.7 µg/ml. Cathelicidin-BF also effectively killed other microorganisms including Staphylococcus epidermidis, which was possible pathogen for acne vulgaris. Cathelicidin-BF significantly inhibited pro-inflammatory factors secretion in human monocytic cells and P. acnes-induced O2.− production of human HaCaT keratinocyte cells. Observed by scanning electron microscopy, the surfaces of the treated pathogens underwent obvious morphological changes compared with the untreated controls, suggesting that this antimicrobial peptide exerts its action by disrupting membranes of microorganisms. The efficacy of cathelicidin-BF gel topical administering was evaluated in experimental mice skin colonization model. In vivo anti-inflammatory effects of cathelicidin-BF were confirmed by relieving P. acnes-induced mice ear swelling and granulomatous inflammation. The anti-inflammatory effects combined with potent antimicrobial activities and O2.− production inhibition activities of cathelicidin-BF indicate its potential as a novel therapeutic option for acne vulgaris
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