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

Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models

This paper introduces a new estimation method for linear dynamic panel data models with endogenous explanatory variables. The proposed approach adapts the estimation methods based on bias corrections of the least-squares dummy-variable or maximum-likelihood estimators to a common situation, where some explanatory variables are endogenous. The estimation approach relies on combining several simple instrumental variable estimators and correcting their biases using the analytically-derived bias expressions. We prove the consistency and asymptotic normality of the proposed bias-corrected instrumental-variable estimator under weak assumptions. The finite sample performance is compared with existing estimators by means of Monte Carlo simulations, which demonstrate good performance with the simplest choice of instrumental variables

Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models

This paper introduces a new estimation method for linear dynamic panel data models with endogenous explanatory variables. The proposed approach adapts the estimation methods based on bias corrections of the least-squares dummy-variable or maximum-likelihood estimators to a common situation, where some explanatory variables are endogenous. The estimation approach relies on combining several simple instrumental variable estimators and correcting their biases using the analytically-derived bias expressions. We prove the consistency and asymptotic normality of the proposed bias-corrected instrumental-variable estimator under weak assumptions. The finite sample performance is compared with existing estimators by means of Monte Carlo simulations, which demonstrate good performance with the simplest choice of instrumental variables