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20172025
most citedAutomatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

196 citations · 305 across the 13 of their papers we have counts for

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physics.ins-det2021★ 77 cited

Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider

Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +11

In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely ch…

physics.ins-det2020★ 196 cited

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Claudionor N. Coelho, Aki Kuusela, Shan Li +7

Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore r…