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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
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar +3
It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activat…