Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. In this paper, we build an artificial intelligence platform for heterogeneous ...
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Conditional connectivity is important for the design of the upper layer communication protocol and the network deployment in different scenarios. This paper analyzes the performance of conditional connectivity with th...
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The interference of performance between the latency-critical tasks and batch tasks leads to a low utilization of the load in the data centers, because of the competition of the system resources to guarantee the latenc...
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The ability to explicitly represent sentences is central to natural language processing. Convolutional neural network (CNN), recurrent neural network and recursive neural networks are mainstream architectures. We intr...
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3D integration enables the implementation of image sensors with integrated neural compute hardware capable of capturing images and performing neural classification in field. The inherent parallelism advantages of 3D s...
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ISBN:
(纸本)9781538676271
3D integration enables the implementation of image sensors with integrated neural compute hardware capable of capturing images and performing neural classification in field. The inherent parallelism advantages of 3D stacking are ideal for high throughput imagers [1] [2] [3] as well neuromorphic hardware [4], [5] which are characterized by their large degree of parallel computation. This work presents a 3D stacked imaging system, composed of a digital pixel array, integrated with ReRAM based neural accelerator. A similar system, Neurosensor, was presented in [6], [7]. However, the imager in Neurosensor adopted a row by row pixel readout mechanism, and the neural accelerator was based on CMOS digital logic. In contrast, this system adopts digital pixels with in-pixel ADC which read out all pixels simultaneously for high throughput imaging, and integrates it with an ReRAM based processing-in-memory (PIM) analog neural accelerator. We present the system architecture and evaluate the various trade-offs in terms of hardware requirements, performance, and energy efficiency, as well as compare our system to Neurosensor.
In high speed railway environment, the LTE system performance evaluation is of vital importance due to complexity of radio propagation scenario and high mobility. In this paper, the Hardware-in-the-Loop (HIL) simulati...
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In this paper, we present an optimized data processing framework: Mimir+. Mimir+ is an implementation of MapReduce over MPI. In order to take full advantage of heterogeneous computing system, we propose the concept of...
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Deep learning has been demonstrated to be very effective in many difficult tasks of computer vision(CV) and natural language processing(NLP). But this usually depends on a large number of available training samples to...
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Single Frequency Network (SFN) is considered as a vital deployment method in high Speed Train (HST) scenario. HST channel model is of much importance to LTE performance assessment. And SFN channel models are non-stati...
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Due to the millimeter wave's high path loss, beamforming is required to provide higher gain. Conventional beamformer is widely used for its simplicity in many other studies. However, it is vulnerable to unexpected...
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