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Deep Learning Inference in Facebook Data Centers (arxiv.org)
3 points by matt_d on Nov 27, 2018 | hide | past | pdf | discuss on HN

In plain words: Facebook measured how its deep-learning models behave when answering requests in its data centers, then tuned the software running them on today's chips. That sped things up, but the chips fall short, so future hardware should be built alongside models and their number formats.

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

The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models used in many Facebook social network services. We present computational characteristics of our models, describe high performance optimizations targeting existing systems, point out their limitations and make suggestions for the future general-purpose/accelerated inference hardware. Also, we highlight the need for better co-design of algorithms, numerics and computing platforms to address the challenges of workloads often run in data centers.

Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, et al.
arXiv:1811.09886 · cs.LG, stat.ML · submitted Nov 24, 2018 · updated Nov 29, 2018
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