Pipeline (computing)

About: Pipeline (computing) is a research topic. Over the lifetime, 26760 publications have been published within this topic receiving 204305 citations. The topic is also known as: data pipeline & computational pipeline.

Electric field calculations in brain stimulation based on finite elements: an optimized processing pipeline for the generation and usage of accurate individual head models.

Abstract: The need for realistic electric field calculations in human noninvasive brain stimulation is undisputed to more accurately determine the affected brain areas. However, using numerical techniques such as the finite element method (FEM) is methodologically complex, starting with the creation of accurate head models to the integration of the models in the numerical calculations. These problems substantially limit a more widespread application of numerical methods in brain stimulation up to now. We introduce an optimized processing pipeline allowing for the automatic generation of individualized high-quality head models from magnetic resonance images and their usage in subsequent field calculations based on the FEM. The pipeline starts by extracting the borders between skin, skull, cerebrospinal fluid, gray and white matter. The quality of the resulting surfaces is subsequently improved, allowing for the creation of tetrahedral volume head meshes that can finally be used in the numerical calculations. The pipeline integrates and extends established (and mainly free) software for neuroimaging, computer graphics, and FEM calculations into one easy-to-use solution. We demonstrate the successful usage of the pipeline in six subjects, including field calculations for transcranial magnetic stimulation and transcranial direct current stimulation. The quality of the head volume meshes is validated both in terms of capturing the underlying anatomy and of the well-shapedness of the mesh elements. The latter is crucial to guarantee the numerical robustness of the FEM calculations. The pipeline will be released as open-source, allowing for the first time to perform realistic field calculations at an acceptable methodological complexity and moderate costs.

Implementing precise interrupts in pipelined processors

Abstract: Five solutions to the precise interrupt problem in pipelined processors are described and evaluated. An interrupt is precise if the saved process state corresponds to a sequential model of program execution in which one instruction completes before the next begins. In a pipelined processor, precise interrupts are difficult to implement because an instruction may be initiated before its predecessors have completed. The first solution forces instructions to complete and modify the process state in architectural order. The other four solutions allow instructions to complete in any order, but additional hardware is used, so that a precise state can be restored when an interrupt occurs. All the methods are discussed in the context of a parallel pipeline structure. Simulation results for the Cray-1S scalar architecture are used to show that the first solution results in a performance degradation of at least 16%. The remaining four solutions offer better performance, and three of them result in as little as a 3% performance loss. Several extensions, including vector architectures, virtual memory, and linear pipeline structures, are briefly discussed. >

Pipeline interleaving and parallelism in recursive digital filters. I. Pipelining using scattered look-ahead and decomposition

Abstract: A look-ahead approach (referred to as scattered look-ahead) to pipeline recursive loops is introduced in a way that guarantees stability. A decomposition technique is proposed to implement the nonrecursive portion (generated due to the scattered look-ahead process) in a decomposed manner to obtain concurrent stable pipelined realizations of logarithmic implementation complexity with respect to the number of loop pipeline stages (as opposed to linear). The upper bound on the roundoff error in these pipelined filters is shown to improve with an increase in the number of loop pipeline stages. Efficient pipelined realizations are studied of both direct-form and state-space-form recursive digital filters. Based on the scattered look-ahead technique, fully pipelined and fully hardware efficient linear bidirectional systolic arrays for recursive digital filters are presented. The decomposition technique is extended to time-varying recursive systems. >

Deep Learning-Based Channel Estimation

Abstract: In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.

Edge Assisted Real-time Object Detection for Mobile Augmented Reality

Abstract: Most existing Augmented Reality (AR) and Mixed Reality (MR) systems are able to understand the 3D geometry of the surroundings but lack the ability to detect and classify complex objects in the real world. Such capabilities can be enabled with deep Convolutional Neural Networks (CNN), but it remains difficult to execute large networks on mobile devices. Offloading object detection to the edge or cloud is also very challenging due to the stringent requirements on high detection accuracy and low end-to-end latency. The long latency of existing offloading techniques can significantly reduce the detection accuracy due to changes in the user's view. To address the problem, we design a system that enables high accuracy object detection for commodity AR/MR system running at 60fps. The system employs low latency offloading techniques, decouples the rendering pipeline from the offloading pipeline, and uses a fast object tracking method to maintain detection accuracy. The result shows that the system can improve the detection accuracy by 20.2%-34.8% for the object detection and human keypoint detection tasks, and only requires 2.24ms latency for object tracking on the AR device. Thus, the system leaves more time and computational resources to render virtual elements for the next frame and enables higher quality AR/MR experiences.