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The architecture of the data acquisition system foreseen for the LHCb upgrade, to be installed by 2018, is devised to readout events trigger-less, synchronously with the LHC bunch crossing rate at 40 MHz. Within this approach the readout boards act as a bridge between the front-end electronics and the High Level Trigger (HLT) computing farm. The baseline design for the LHCb readout is an ATCA board requiring dedicated crates. A local area standard network protocol is implemented in the on-board FPGAs to read out the data. The alternative solution proposed here consists in building the readout boards as PCIe peripherals of the event-builder servers. The main architectural advantage is that protocol and link-technology of the event-builder can be left open until very late, to profit from the most cost-effective industry technology available at the time of the LHC LS2.

This article presents a design methodology for designing an artificial neural network as an equalizer for a binary signal. Firstly, the system is modelled in floating point format using Matlab. Afterward, the design is described for a Field Programmable Gate Array (FPGA) using fixed point format. The FPGA design is based on the System Generator from Xilinx, which is a design tool over Simulink of Matlab. System Generator allows one to design in a fast and flexible way. It uses low level details of the circuits and the functionality of the system can be fully tested. System Generator can be used to check the architecture and to analyse the effect of the number of bits on the system performance. Finally the System Generator design is compiled for the Xilinx Integrated System Environment (ISE) and the system is described using a hardware description language. In ISE the circuits are managed with high level details and physical performances are obtained. In the Conclusions section, some modifications are proposed to improve the methodology and to ensure portability across FPGA manufacturers.