Less is More: Compression of Deep Neural Networks for adaptation in photonic FPGA circuits

Eftychia Makri, Georgios Agrafiotis, Ilias Kalamaras, Antonios Lalas, Konstantinos Votis, Dimitrios Tzovaras

Abstract: Photonic circuits pave the way to ultrafast computing and real-time inference of applications with paramount importance, such as imaging flow cytometry (IFC). However, current implementations exhibit inherent restrictions that consequently diminish the neural networks (NNs) complexity that can be supported.

External IDs:dblp:conf/dcc/MakriAKLVT22