Optimization on fixed low latency implementation of GBT protocol in FPGA

In the upgrade of ATLAS experiment, the front-end electronics components are subjected to a large radiation background. Meanwhile high speed optical links are required for the data transmission between the on-detector and off-detector electronics. The GBT architecture and the Versatile Link (VL) project are designed by CERN to support the 4.8 Gbps line rate bidirectional high-speed data transmission which is called GBT link. In the ATLAS upgrade, besides the link with on-detector, the GBT link is also used between different off-detector systems. The GBTX ASIC is designed for the on-detector front-end, correspondingly for the off-detector electronics, the GBT architecture is implemented in Field Programmable Gate Arrays (FPGA). CERN launches the GBT-FPGA project to provide examples in different types of FPGA. In the ATLAS upgrade framework, the Front-End LInk eXchange (FELIX) system is used to interface the front-end electronics of several ATLAS subsystems. The GBT link is used between them, to transfer the detector data and the timing, trigger, control and monitoring information. The trigger signal distributed in the down-link from FELIX to the front-end requires a fixed and low latency. In this paper, several optimizations on the GBT-FPGA IP core are introduced, to achieve a lower fixed latency. For FELIX, a common firmware will be used to interface different front-ends with support of both GBT modes: the forward error correction mode and the wide mode. The modified GBT-FPGA core has the ability to switch between the GBT modes without FPGA reprogramming. The system clock distribution of the multi-channel FELIX firmware is also discussed in this paper

RANKING RESOURCES REFERRING TO LOCATIONS

A system and method ranks resources referring to locations. In one aspect, the method includes generating a location graph, the graph including resource nodes representing resources, location nodes representing geographic locations, and edges between resource nodes and location nodes. Each edge between the resource nodes and the location nodes represents a reference in the resource represented by the resource node to the geographic location represented by the location node. The method includes initializing scores in the location graph for resource nodes or location nodes or both; calculating a score for each resource node based on scores for location nodes sharing an edge with the resource node; and calculating a score for each location node based on scores for resource nodes sharing an edge with the location node

The phase diagram of kernel interpolation in large dimensions

The generalization ability of kernel interpolation in large dimensions (i.e., $n \asymp d^{\gamma}$ for some $\gamma>0$) might be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the 'benign overfitting phenomenon' reported in the neural networks literature. Focusing on the inner product kernel on the sphere, we fully characterized the exact order of both the variance and bias of large-dimensional kernel interpolation under various source conditions $s\geq 0$. Consequently, we obtained the $(s,\gamma)$-phase diagram of large-dimensional kernel interpolation, i.e., we determined the regions in $(s,\gamma)$-plane where the kernel interpolation is minimax optimal, sub-optimal and inconsistent.Comment: 18 pages, 1 figur

SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution

Semi-supervised video object segmentation (Semi-VOS), which requires only annotating the first frame of a video to segment future frames, has received increased attention recently. Among existing pipelines, the memory-matching-based one is becoming the main research stream, as it can fully utilize the temporal sequence information to obtain high-quality segmentation results. Even though this type of method has achieved promising performance, the overall framework still suffers from heavy computation overhead, mainly caused by the per-frame dense convolution operations between high-resolution feature maps and each kernel filter. Therefore, we propose a sparse baseline of VOS named SpVOS in this work, which develops a novel triple sparse convolution to reduce the computation costs of the overall VOS framework. The designed triple gate, taking full consideration of both spatial and temporal redundancy between adjacent video frames, adaptively makes a triple decision to decide how to apply the sparse convolution on each pixel to control the computation overhead of each layer, while maintaining sufficient discrimination capability to distinguish similar objects and avoid error accumulation. A mixed sparse training strategy, coupled with a designed objective considering the sparsity constraint, is also developed to balance the VOS segmentation performance and computation costs. Experiments are conducted on two mainstream VOS datasets, including DAVIS and Youtube-VOS. Results show that, the proposed SpVOS achieves superior performance over other state-of-the-art sparse methods, and even maintains comparable performance, e.g., an 83.04% (79.29%) overall score on the DAVIS-2017 (Youtube-VOS) validation set, with the typical non-sparse VOS baseline (82.88% for DAVIS-2017 and 80.36% for Youtube-VOS) while saving up to 42% FLOPs, showing its application potential for resource-constrained scenarios.Comment: 15 pages, 6 figure

Optimal Rate of Kernel Regression in Large Dimensions

We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i.e., $n\asymp d^{\gamma}$ for some $\gamma >0$ ). We first build a general tool to characterize the upper bound and the minimax lower bound of kernel regression for large dimensional data through the Mendelson complexity $\varepsilon_{n}^{2}$ and the metric entropy $\bar{\varepsilon}_{n}^{2}$ respectively. When the target function falls into the RKHS associated with a (general) inner product model defined on $\mathbb{S}^{d}$, we utilize the new tool to show that the minimax rate of the excess risk of kernel regression is $n^{-1/2}$ when $n\asymp d^{\gamma}$ for $\gamma =2, 4, 6, 8, \cdots$. We then further determine the optimal rate of the excess risk of kernel regression for all the $\gamma>0$ and find that the curve of optimal rate varying along $\gamma$ exhibits several new phenomena including the {\it multiple descent behavior} and the {\it periodic plateau behavior}. As an application, For the neural tangent kernel (NTK), we also provide a similar explicit description of the curve of optimal rate. As a direct corollary, we know these claims hold for wide neural networks as well

Real-time spatial frequency domain imaging by single snapshot multiple frequency demodulation technique

We have presented a novel Single Snapshot Multiple Frequency Demodulation (SSMD) method enabling single snapshot wide field imaging of optical properties of turbid media in the Spatial Frequency Domain. SSMD makes use of the orthogonality of harmonic functions and extracts the modulation transfer function (MTF) at multiple modulation frequencies and of arbitrary orientations and amplitudes simultaneously from a single structured-illuminated image at once. SSMD not only increases significantly the data acquisition speed and reduces motion artifacts but also exhibits excellent noise suppression in imaging as well. The performance of SSMD-SFDI is demonstrated with experiments on both tissue mimicking phantoms and in vivo for recovering optical properties. SSMD is ideal in the implementation of a real-time spatial frequency domain imaging platform, which will open up SFDI for vast applications in, for example, mapping the optical properties of a dynamic turbid medium or monitoring fast temporal evolutions. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Single snapshot multiple frequency modulated imaging of subsurface optical properties of turbid media with structured light

We report a novel demodulation method that enables single snapshot wide field imaging of optical properties of turbid media in the Spatial Frequency Domain (SFD). This Single Snapshot Multiple frequency Demodulation (SSMD) method makes use of the orthogonality of harmonic functions to extract the modulation transfer function (MTF) at multiple modulation frequencies simultaneously from a single structured-illuminated image at once. The orientation, frequency, and amplitude of each modulation can be set arbitrarily subject to the limitation of the implementation device. We first validate and compare SSMD to the existing demodulation methods by numerical simulations. The performance of SSMD is then demonstrated with experiments on both tissue mimicking phantoms and in vivo for recovering optical properties by comparing to the standard three-phase demodulation approach. The results show that SSMD increases significantly the data acquisition speed and reduces motion artefacts. SSMD exhibits excellent noise suppression in imaging as well at the rate proportional to the square root of the number of pixels contained in its kernel. SSMD is ideal in the implementation of a real-time spatial frequency domain imaging platform and will open up SFDI for vast applications in imaging and monitoring dynamic turbid medium and processes