Query: Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

Relevant passages:
Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

Deep Learning with Limited Numerical Precision <=SEP=> Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network’s behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.

Learning Deep Features for Scene Recognition using Places Database <=SEP=> [1] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 2007. [2] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. 2007. [3] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006. [4] L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In Proc. ICCV, 2007. [5] G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In Proc. CVPR, 2012.

A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks <=SEP=> Deep networks are state-of-the-art models used for understanding the content of images, videos, audio and raw input data. Current computing systems are not able to run deep network models in real-time with low power consumption. In this paper we present nn-X: a scalable, low-power coprocessor for enabling real-time execution of deep neural networks. nn-X is implemented on programmable logic devices and comprises an array of configurable processing elements called collections. These collections perform the most common operations in deep networks: convolution, subsampling and non-linear functions. The nn-X system includes 4 high-speed direct memory access interfaces to DDR3 memory and two ARM Cortex-A9 processors. Each port is capable of a sustained throughput of 950 MB/s in full duplex. nn-X is able to achieve a peak performance of 227 G-ops/s, a measured performance in deep learning applications of up to 200 G-ops/s while consuming less than 4 watts of power. This translates to a performance per power improvement of 10 to 100 times that of conventional mobile and desktop processors.

Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks <=SEP=> Convolutional neural network (CNN) has been widely employed for image recognition because it can achieve high accuracy by emulating behavior of optic nerves in living creatures. Recently, rapid growth of modern applications based on deep learning algorithms has further improved research and implementations. Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, the accelerator design space has not been well exploited. One critical problem is that the computation throughput may not well match the memory bandwidth provided an FPGA platform. Consequently, existing approaches cannot achieve best performance due to under-utilization of either logic resource or memory bandwidth. At the same time, the increasing complexity and scalability of deep learning applications aggravate this problem. In order to overcome this problem, we propose an analytical design scheme using the roofline model. For any solution of a CNN design, we quantitatively analyze its computing throughput and required memory bandwidth using various optimization techniques, such as loop tiling and transformation. Then, with the help of rooine model, we can identify the solution with best performance and lowest FPGA resource requirement. As a case study, we implement a CNN accelerator on a VC707 FPGA board and compare it to previous approaches. Our implementation achieves a peak performance of 61.62 GFLOPS under 100MHz working frequency, which outperform previous approaches significantly.

Topology Derivation of Nonisolated Three-Port DC–DC Converters From DIC and DOC <=SEP=> A systematic approach is proposed for the derivation of nonisolated three-port converter (TPC) topologies based on dual-input converters (DIC) and dual-output converters (DOC), which serves as an interface for a renewable source, a storage battery, and a load simultaneously. The power flow in a TPC is analyzed and compared with that in a DIC or a DOC, respectively. Beginning with building the power flow paths of a TPC from a DIC or a DOC, the general principles and detailed procedures for the generation and optimization of TPC topologies are presented. Based on these works, a family of nonisolated TPC topologies is developed. The derived TPCs feature single-stage power conversion between any two of the three ports, and result in high integration and high efficiency. One of the proposed TPCs, named Boost-TPC, is taken as an example for verifying the performance of its circuit topology with related operational methods. Pulsewidth modulation and power management methods, used in this circuit, are analyzed in detail. Experiments have been carried out on a 1-kW prototype of the Boost-TPC, which demonstrate the feasibility and effectiveness of the proposed topology derivation method.

A framework for cognitive Internet of Things based on blockchain <=SEP=> Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.

Management patterns: SDN-enabled network resilience management <=SEP=> Software-defined networking provides abstractions and a flexible architecture for the easy configuration of network devices, based on the decoupling of the data and control planes. This separation has the potential to considerably simplify the implementation of resilience functionality (e.g., traffic classification, anomaly detection, traffic shaping) in future networks. Although software-defined networking in general, and OpenFlow as its primary realisation, provide such abstractions, support is still needed for orchestrating a collection of OpenFlow-enabled services that must cooperate to implement network-wide resilience. In this paper, we describe a resilience management framework that can be readily applied to this problem. An important part of the framework are policy-controlled management patterns that describe how to orchestrate individual resilience services, implemented as OpenFlow applications.

Gated convolutional networks based hybrid acoustic models for low resource speech recognition <=SEP=> In acoustic modeling for large vocabulary speech recognition, recurrent neural networks (RNN) have shown great abilities to model temporal dependencies. However, the performance of RNN is not prominent in resource limited tasks, even worse than the traditional feedforward neural networks (FNN). Furthermore, training time for RNN is much more than that for FNN. In recent years, some novel models are provided. They use non-recurrent architectures to model long term dependencies. In these architectures, they show that using gate mechanism is an effective method to construct acoustic models. On the other hand, it has been proved that using convolution operation is a good method to learn acoustic features. We hope to take advantages of both these two methods. In this paper we present a gated convolutional approach to low resource speech recognition tasks. The gated convolutional networks use convolutional architectures to learn input features and a gate to control information. Experiments are conducted on the OpenKWS, a series of low resource keyword search evaluations. From the results, the gated convolutional networks relatively decrease the WER about 6% over the baseline LSTM models, 5% over the DNN models and 3% over the BLSTM models. In addition, the new models accelerate the learning speed by more than 1.8 and 3.2 times compared to that of the baseline LSTM and BLSTM models.

Deep Texture Features for Robust Face Spoofing Detection <=SEP=> Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

Performance Evaluation of Concurrent Lock-free Data Structures on GPUs <=SEP=> Graphics processing units (GPUs) have emerged as a strong candidate for high-performance computing. While regular data-parallel computations with little or no synchronization are easy to map on the GPU architectures, it is a challenge to scale up computations on dynamically changing pointer-linked data structures. The traditional lock-based implementations are known to offer poor scalability due to high lock contention in the presence of thousands of active threads, which is common in GPU architectures. In this paper, we present a performance evaluation of concurrent lock-free implementations of four popular data structures on GPUs. We implement a set using lock-free linked list, hash table, skip list, and priority queue. On the first three data structures, we evaluate the performance of different mixes of addition, deletion, and search operations. The priority queue is designed to support retrieval and deletion of the minimum element and addition operations to the set. We evaluate the performance of these lock-free data structures on a Tesla C2070 Fermi GPU and compare it with the performance of multi-threaded lock-free implementations for CPU running on a 24-core Intel Xeon server. The linked list, hash table, skip list, and priority queue implementations achieve speedup of up to 7.4, 11.3, 30.7, and 30.8, respectively on the GPU compared to the Xeon server.

Images of Success and the Preference for Luxury <=SEP=> This research examines the impact of media depictions of success (or failure) on consumers’ desire for luxury brands. In a pilot study and three additional studies, we demonstrate that reading a story about a similar/successful other, such as a business major from the same university, increases consumers’ expectations about their own future wealth, which in turn increases their desire for luxury brands. However, reading about a dissimilar successful other, such as a biology major, lowers consumers’preferences for luxury brands. Furthermore, we examine the role of ease of imagining oneself in the narrative as a mediator of the relation between direction of comparison, similarity, and brand preference.

A new weight initialization method for sigmoidal feedforward artificial neural networks <=SEP=> Initial weight choice has been recognized to be an important aspect of the training methodology for sigmoidal feedforward neural networks. In this paper, a new mechanism for weight initialization is proposed. The mechanism distributes the initial input to output weights in a manner that all weights (including thresholds) leading into a hidden layer are uniformly distributed in a region and the center of the region from which the weights are sampled are such that no region overlaps for two distinct hidden nodes. The proposed method is compared against random weight initialization routines on five function approximation tasks using the Resilient Backpropagation (RPROP) algorithm for training. The proposed method is shown to lead to about twice as fast convergence to a pre-specifled goal for training as compared to any of the random weight initialization methods. Moreover, it is shown that at least for these problems the networks reach a deeper minima of the error functional during training and generalizes better than the networks trained whose weights were initialized by random weight initialization methods.

Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies. <=SEP=> Researchers have increasingly turned to mixed-method techniques to expand the scope and improve the analytic power of their studies. Yet there is still relatively little direction on and much confusion about how to combine qualitative and quantitative techniques. These techniques are neither paradigm- nor method-linked; researchers' orientations to inquiry and their methodological commitments will influence how they use them. Examples of sampling combinations include criterion sampling from instrument scores, random purposeful sampling, and stratified purposeful sampling. Examples of data collection combinations include the use of instruments for fuller qualitative description, for validation, as guides for purposeful sampling, and as elicitation devices in interviews. Examples of data analysis combinations include interpretively linking qualitative and quantitative data sets and the transformation processes of qualitizing and quantitizing.

Artistic reality: fast brush stroke stylization for augmented reality <=SEP=> The goal of augmented reality is to provide the user with a view of the surroundings enriched by virtual objects. Practically all augmented reality systems rely on standard real-time rendering methods for displaying graphical objects. Although such conventional computer graphics algorithms are fast, they often fail to produce sufficiently realistic renderings. Therefore, virtual models can easily be distinguished from the real environment. We have recently proposed a novel approach for generating augmented reality images [4]. Our method is based on the idea of applying stylization techniques for adapting the visual realism of both the camera image and the virtual graphical objects. Since both the camera image and the virtual objects are stylized in a corresponding way, they appear very similar. Here, we present a new method for the stylization of augmented reality images. This approach generates a painterly brush stroke rendering. The resulting stylized augmented reality video frames look similar to paintings created in the pointillism style. We describe the implementation of the camera image filter and the non-photorealistic rendererfor virtual objects. These components have been newly designed or adapted for this purpose. They are fast enough for generating augmented reality images in near real-time (more than 14 frames per second).

A Definition of Artificial Intelligence <=SEP=> In this paper we offer a formal definition of Artificial Intelligence and this directly gives us an algorithm for construction of this object. Really, this algorithm is useless due to the combinatory explosion. The main innovation in our definition is that it does not include the knowledge as a part of the intelligence. So according to our definition a newly born baby also is an Intellect. Here we differs with Turing's definition which suggests that an Intellect is a person with knowledge gained through the years.

Anisotropic diffusion map based spectral embedding for 3D CAD model retrieval <=SEP=> In the product life cycle, design reuse can save cost and improve existing products conveniently in most new product development. To retrieve similar models from big database, most search algorithms convert CAD model into a shape descriptor and compute the similarity two models according to a descriptor metric. This paper proposes a new 3D shape matching approach by matching the coordinates directly. It is based on diffusion maps which integrate the rand walk and graph spectral analysis to extract shape features embedded in low dimensional spaces and then they are used to form coordinations for non-linear alignment of different models. These coordinates could capture multi-scale properties of the 3D geometric features and has shown good robustness to noise. The results also have shown better performance compared to the celebrated Eigenmap approach in the 3D model retrieval.

Know Your Body Through Intrinsic Goals <=SEP=> The first "object" that newborn children play with is their own body. This activity allows them to autonomously form a sensorimotor map of their own body and a repertoire of actions supporting future cognitive and motor development. Here we propose the theoretical hypothesis, operationalized as a computational model, that this acquisition of body knowledge is not guided by random motor-babbling, but rather by autonomously generated goals formed on the basis of intrinsic motivations. Motor exploration leads the agent to discover and form representations of the possible sensory events it can cause with its own actions. When the agent realizes the possibility of improving the competence to re-activate those representations, it is intrinsically motivated to select and pursue them as goals. The model is based on four components: (1) a self-organizing neural network, modulated by competence-based intrinsic motivations, that acquires abstract representations of experienced sensory (touch) changes; (2) a selector that selects the goal to pursue, and the motor resources to train to pursue it, on the basis of competence improvement; (3) an echo-state neural network that controls and learns, through goal-accomplishment and competence, the agent's motor skills; (4) a predictor of the accomplishment of the selected goals generating the competence-based intrinsic motivation signals. The model is tested as the controller of a simulated simple planar robot composed of a torso and two kinematic 3-DoF 2D arms. The robot explores its body covered by touch sensors by moving its arms. The results, which might be used to guide future empirical experiments, show how the system converges to goals and motor skills allowing it to touch the different parts of own body and how the morphology of the body affects the formed goals. The convergence is strongly dependent on competence-based intrinsic motivations affecting not only skill learning and the selection of formed goals, but also the formation of the goal representations themselves.

Photo tourism: exploring photo collections in 3D <=SEP=> We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Design for wearability <=SEP=> Digital Technology is constantly improving as information becomes wireless. These advances demand more wearable and mobile form factors for products that access information. A product that is wearable should have wearability. This paper explores the concept of dynamic wearability, through design research. Wearability is defined as the interaction between the human body and the wearable object. Dynamic wearability extends that definition to include the human body in motion. Our research has been to locate, understand, and define the spaces on the human body where solid and flexible forms can rest-without interfering with fluid human movement. The result is a set of design guidelines embodied in a set of wearable forms. These wearable forms describe the three dimensional spaces on the body best suited for comfortable and unobtrusive wearability by design.

A 3.4 W Digital-In Class-D Audio Amplifier in 0.14 $\mu $m CMOS <=SEP=> In this paper a class-D audio amplifier for mobile applications is presented realized in a 0.14 μm CMOS technology tailored for mobile applications. The amplifier has a simple PDM-based digital interface for audio and control that requires only two pins and enables assembly in 9-bump WL-CSP. The complete audio path is discussed that consists of a Parser, Digital PWM controller, 1-bit DA-converters, analog feedback loop and the Class-D power stage. A reconfigurable gate driver is used that reduces quiescent current consumption and radiated emission.

Differential effects of atomoxetine on executive functioning and lexical decision in attention-deficit/hyperactivity disorder and reading disorder. <=SEP=> OBJECTIVE
The effects of a promising pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), atomoxetine, were studied on executive functions in both ADHD and reading disorder (RD) because earlier research demonstrated an overlap in executive functioning deficits in both disorders. In addition, the effects of atomoxetine were explored on lexical decision.


METHODS
Sixteen children with ADHD, 20 children with ADHD + RD, 21 children with RD, and 26 normal controls were enrolled in a randomized placebo-controlled crossover study. Children were measured on visuospatial working memory, inhibition, and lexical decision on the day of randomization and following two 28-day medication periods.


RESULTS
Children with ADHD + RD showed improved visuospatial working memory performance and, to a lesser extent, improved inhibition following atomoxetine treatment compared to placebo. No differential effects of atomoxetine were found for lexical decision in comparison to placebo. In addition, no effects of atomoxetine were demonstrated in the ADHD and RD groups.


CONCLUSION
Atomoxetine improved visuospatial working memory and to a lesser degree inhibition in children with ADHD + RD, which suggests differential developmental pathways for co-morbid ADHD + RD as compared to ADHD and RD alone.


CLINICAL TRIAL REGISTRY
B4Z-MC-LYCK, NCT00191906; http://clinicaltrials.gov/ct2/show/NCT00191906.

A New Approach to Quantify Network Security by Ranking of Security Metrics and Considering Their Relationships <=SEP=> There are several characteristics in computer networks, which play important roles in determining the level of network security. These characteristics known as security metrics can be applied for security quantification in computer networks. Most of the researches on this area has focused on defining the new security metrics to improve the quantification process. In this paper, we present a new approach to analyze and quantify the network security by ranking of security metrics with considering the relationships between them. Our ranking method reveals the importance of each security metric to quantify security in the network under surveillance. The proposed approach helps the network administrators to have a better insight on the level of network security

Is FLIP enough? Or should we use the FLIPPED model instead? <=SEP=> Yunglung Chen, Yuping Wang, Kinshuk, Nian-Shing Chen Department of Information Management, National Sun Yat-sen University, No. 70, Lienhai Rd., Kaohsiung 80424, Taiwan School of Languages and Linguistics, Griffith University, Nathan, 4111, Australia School of Computing and Information System, Athabasca University, Athabasca, Canada henryylchen@gmail.com, y.wang@griffith.edu.au, kinshuk@athabascau.ca, nschen@mis.nsysu.edu.tw

Step-by-step design and control of LCL filter based three phase grid-connected inverter <=SEP=> This paper proposes a detailed step-by-step design procedure and control of an LCL filter for grid connected three phase sine PWM voltage source inverter. The goal of the design is to ensure high quality of grid current as well as to minimize the size of filter magnetics. In order to ensure unity power factor injection into grid a current controller is designed with a constraint that only the inverter side inductor current is sensed. Good agreement between simulation and experimental results verify the effectiveness of the designed controller and the LCL filter.

Power Quality Control and Design of Power Converter for Variable-Speed Wind Energy Conversion System with Permanent-Magnet Synchronous Generator <=SEP=> The control strategy and design of an AC/DC/AC IGBT-PMW power converter for PMSG-based variable-speed wind energy conversion systems (VSWECS) operation in grid/load-connected mode are presented. VSWECS consists of a PMSG connected to a AC-DC IGBT-based PWM rectifier and a DC/AC IGBT-based PWM inverter with LCL filter. In VSWECS, AC/DC/AC power converter is employed to convert the variable frequency variable speed generator output to the fixed frequency fixed voltage grid. The DC/AC power conversion has been managed out using adaptive neurofuzzy controlled inverter located at the output of controlled AC/DC IGBT-based PWM rectifier. In this study, the dynamic performance and power quality of the proposed power converter connected to the grid/load by output LCL filter is focused on. Dynamic modeling and control of the VSWECS with the proposed power converter is performed by using MATLAB/Simulink. Simulation results show that the output voltage, power, and frequency of VSWECS reach to desirable operation values in a very short time. In addition, when PMSG based VSWECS works continuously with the 4.5 kHz switching frequency, the THD rate of voltage in the load terminal is 0.00672%.

Finding expert users in community question answering <=SEP=> Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.

Impact of rank optimization on downlink non-orthogonal multiple access (NOMA) with SU-MIMO <=SEP=> Non-orthogonal multiple access (NOMA) is a promising radio access technique for further cellular enhancements toward the 5th generation (5G) mobile communication systems. Single-user multiple-input multiple-output (SU-MIMO) is one of the key technologies in long term evolution (LTE) /LTE-Advanced systems. It is proved that NOMA combined with SU-MIMO techniques can achieve further system performance improvement. In this paper, we focus on the impact of rank optimization on the performance of NOMA with SU-MIMO in downlink. Firstly, a geometry based rank adjustment method is studied. Secondly, an enhanced feedback method for rank adjustment is discussed. The simulation results show that the performance gain of NOMA improves with the proposed two rank adjustment methods. Compared to orthogonal access system, large performance gains can be achieved for NOMA, which are about 23% for cell average throughput and 33% for cell-edge user throughput.

On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality <=SEP=> The classification problem is considered in which an outputvariable y assumes discrete values with respectiveprobabilities that depend upon the simultaneous values of a set of input variablesx = {x_1,....,x_n}. At issue is how error in the estimates of theseprobabilities affects classification error when the estimates are used ina classification rule. These effects are seen to be somewhat counterintuitive in both their strength and nature. In particular the bias andvariance components of the estimation error combine to influenceclassification in a very different way than with squared error on theprobabilities themselves. Certain types of (very high) bias can becanceled by low variance to produce accurate classification. This candramatically mitigate the effect of the bias associated with some simpleestimators like “naive” Bayes, and the bias induced by thecurse-of-dimensionality on nearest-neighbor procedures. This helps explainwhy such simple methods are often competitive with and sometimes superiorto more sophisticated ones for classification, and why“bagging/aggregating” classifiers can often improveaccuracy. These results also suggest simple modifications to theseprocedures that can (sometimes dramatically) further improve theirclassification performance.

Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module <=SEP=> Semantic segmentation provides a practical way to segment remotely sensed images into multiple ground objects simultaneously, which can be potentially applied to multiple remote sensed related aspects. Current classification algorithms in remotely sensed images are mostly limited by different imaging conditions, the multiple ground objects are difficult to be separated from each other due to high intraclass spectral variances and interclass spectral similarities. In this study, we propose an end-to-end framework to semantically segment high-resolution aerial images without postprocessing to refine the segmentation results. The framework provides a pixel-wise segmentation result, comprising convolutional neural network structure and pyramid pooling module, which aims to extract feature maps at multiple scales. The proposed model is applied to the ISPRS Vaihingen benchmark dataset from the ISPRS 2D Semantic Labeling Challenge. Its segmentation results are compared with previous state-of-the-art method UZ_1, UPB and three other methods that segment images into objects of all the classes (including clutter/background) based on true orthophoto tiles, and achieve the highest overall accuracy of 87.8% over the published performances, to the best of our knowledge. The results validate the efficiency of the proposed model in segmenting multiple ground objects from remotely sensed images simultaneously.