To meet the computational demands required of deep learning, cloud operators are turning toward specialized hardware for improved efficiency and performance. Project Brainwave, Microsofts principal infrastructure for AI serving in real time, accelerates deep neural network (DNN) inferencing in major services such as Bings 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 teraflops (Tflops) of effective performance at Batch 1 on a state-of-the-art Intel Stratix 10 FPGA.

In 2015, a team of software and hardware developers at Microsoft shipped the world?s first commercial search engine accelerated using FPGAs in the datacenter. During the sprint to production, new algorithms in the Bing ranking service were ported into FPGAs and deployed to a production bed within several weeks of conception, leading to significant gains in latency and throughput. The fast turnaround time of new features demanded by an agile software culture would not have been possible without a disciplined and effective approach to co-design in the datacenter. This talk will describe some of the learnings and best practices developed from this unique experience.