  We apply object detection techniques based on deep convolutional blocks to
end-to-end jet identification and reconstruction tasks encountered at the CERN
Large Hadron Collider (LHC). Collision events produced at the LHC and
represented as an image composed of calorimeter and tracker cells are given as
an input to a Single Shot Detection network. The algorithm, named PFJet-SSD
performs simultaneous localization, classification and regression tasks to
cluster jets and reconstruct their features. This all-in-one single
feed-forward pass gives advantages in terms of execution time and an improved
accuracy w.r.t. traditional rule-based methods. A further gain is obtained from
network slimming, homogeneous quantization, and optimized runtime for meeting
memory and latency constraints of a typical real-time processing environment.
We experiment with 8-bit and ternary quantization, benchmarking their accuracy
and inference latency against a single-precision floating-point. We show that
the ternary network closely matches the performance of its full-precision
equivalent and outperforms the state-of-the-art rule-based algorithm. Finally,
we report the inference latency on different hardware platforms and discuss
future applications.
