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cs.CV2023
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
Shivam Aggarwal, Hans Jakob Damsgaard, Alessandro Pappalardo +4
Post-training quantization (PTQ) is a powerful technique for model compression, reducing the numerical precision in neural networks without additional training overhead. Recent wor…
cs.CV2018
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic
Michaela Blott, Thomas B. Preusser, Nicholas Fraser +4
Convolutional Neural Networks have dramatically improved in recent years, surpassing human accuracy on certain problems and performance exceeding that of traditional computer visio…