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2018

Fast inference of deep neural networks in FPGAs for particle physics

Duarte, Javier, Han, Song, Harris, Philip et al.

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Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques.

Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole.
However, exploration of the use of such techniques in low-latency, low-power FPGA hardware has only just begun.
FPGA-based trigger and data acquisition (DAQ) systems have extremely low, sub-microsecond latency requirements that are unique to particle physics.

Fast inference of deep neural networks in FPGAs for particle physics · Around

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