most citedFBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

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cs.LG2021★ 20 cited

FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Daya Khudia, Jianyu Huang, Protonu Basu +4

Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that com…

cs.LG2018

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

Jongsoo Park, Maxim Naumov, Protonu Basu +25

The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models…