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.

本文详细介绍了Facebook社交网络服务中使用的深度学习模型的计算特征，并描述了针对现有系统的高性能优化，同时指出了其局限性，并为未来的通用/加速推理硬件提出建议。此外，我们强调了算法、数字和计算平台更好的协同设计来解决数据中心中经常运行的工作负载的挑战。

Facebook数据中心中的深度学习推理：特征描述、性能优化和硬件影响