Speaker
Description
The slowdown of Moore’s law and the growing requirements of future HEP experiments with ever-increasing data rates pose important computational challenges for data reconstruction and trigger systems, encouraging the exploration of new computing methodologies.
In this talk we discuss a FPGA-based tracking system, based on a massively parallel pattern recognition approach, inspired by the processing of visual images by the natural brain ("retina architecture"). This method allows a large efficiency of utilization of the hardware, low power consumption and very low latencies. Based on this approach, a device has been designed within the LHCb Upgrade-II project, with the goal of performing track reconstruction in the forward acceptance region in real-time during the upcoming Run 4 of the LHC. This innovative device will perform track reconstruction before the event-building, in a short enough time to provide pre-reconstructed tracks ("primitives") transparently to the processor farm, as if they had been generated directly by the detector. This allows significant savings in higher-level computing resources, enabling handling higher luminosities than otherwise possible. The feasibility of the project is backed up by the results of tests performed on a realistic hardware prototype, that has been processing actual LHCb data during the 2023 run, operating in parallel with the regular DAQ chain of the experiment.
| Role of Submitter | I am the presenter |
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