The ability to execute Deep Neural Networks at the trigger level to improve online selection performance will be crucial for current and future high-energy physics experiments. Low-latency hardware solutions exist, e.g. FPGAs, but the primary constraint to the implementation is often related to the model’s size, which has to be finely tuned not to exceed the available memory. We investigate novel approaches to reduce the size of models, having under control the model performances [1].

[1] Di Luca A. , Mascione D., Follega F. M., Cristoforetti M., Iuppa R. , Deep Neural Network resizing for real-time applications in High Energy Physics, Published in: PoS LHCP2021 (2021), 257, DOI: 10.22323/1.397.0257