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DiffLUT-Net

model1 events
papersSEP 10 04:00 UTC

DiffLUT-Net Trains FPGA Lookup-Table Networks End-to-End with Learnable Connectivity

Researchers have introduced DiffLUT-Net, a framework that trains neural networks made of FPGA lookup tables directly through differentiable methods rather than converting pretrained quantized models. The approach also learns the connectivity structure of the LUT network, aiming to make hardware-efficient inference on FPGAs more effective. The work is available as a paper on arXiv.

fpga/hardware/research