Serving DNNs in Real Time at Datacenter Scale with Project Brainwave
- Eric Chung ,
- Jeremy Fowers ,
- Kalin Ovtcharov ,
- Michael Papamichael ,
- Adrian Caulfield ,
- Todd Massengill ,
- Ming Liu ,
- Mahdi Ghandi ,
- Daniel Lo ,
- Steve Reinhardt ,
- Shlomi Alkalay ,
- Hari Angepat ,
- Derek Chiou ,
- Alessandro Forin ,
- Doug Burger ,
- Lisa Woods ,
- Gabriel Weisz ,
- Michael Haselman ,
- Dan Zhang
IEEE Micro | , Vol 38: pp. 8-20
To meet the computational demands required of deep learning, cloud operators are turning toward specialized hardware for improved efficiency and performance. Project Brainwave, Microsoft’s principal infrastructure for AI serving in real time, accelerates deep neural network (DNN) inferencing in major services such as Bing’s intelligent search features and Azure. Exploiting distributed model parallelism and pinning over low-latency hardware microservices, Project Brainwave serves state-of-the-art, pre-trained DNN models with high efficiencies at low batch sizes. A high-performance, precision-adaptable FPGA soft processor is at the heart of the system, achieving up to 39.5 TFLOPs of effective performance at Batch 1 on a state-of-the-art Intel Stratix 10 FPGA.