Microsoft unveils Project Brainwave for real-time AI

Today at Hot Chips 2017, our cross-Microsoft team unveiled a new deep learning acceleration platform, codenamed Project Brainwave. I’m delighted to share more details in this post, since Project Brainwave achieves a major leap forward in both performance and flexibility for cloud-based serving of deep learning models. We designed the system for real-time AI, which means the system processes requests as fast as it receives them, with ultra-low latency. Real-time AI is becoming increasingly important as cloud infrastructures process live data streams, whether they be search queries, videos, sensor streams, or interactions with users. First, Project Brainwave leverages the massive FPGA infrastructure that Microsoft has been deploying over the past few years. By attaching high-performance FPGAs directly to our datacenter network, we can serve DNNs as hardware microservices, where a DNN can be mapped to a pool of remote FPGAs and called by a server with no software in the loop. This system architecture both reduces latency, since the CPU does not need to process incoming requests, and allows very high throughput, with the FPGA processing requests as fast as the network can stream them. Second, Project Brainwave uses a powerful “soft” DNN processing unit (or DPU), synthesized onto commercially available FPGAs. A number of companies—both large companies and a slew of startups—are building hardened DPUs. Although some of these chips have high peak performance, they must choose their operators and data types at design time, which limits their flexibility. Project Brainwave takes a different approach, providing a design that scales across a range of data types, with the desired data type being a synthesis-time decision. The design combines both the ASIC digital signal processing blocks on the FPGAs and the synthesizable logic to provide a greater and more optimized number of functional units. This approach exploits the FPGA’s flexibility in two ways. First, we have defined highly customized, narrow-precision data types that increase performance without real losses in model accuracy. Second, we can incorporate research innovations into the hardware platform quickly (typically a few weeks), which is essential in this fast-moving space. As a result, we achieve performance comparable to – or greater than – many of these hard-coded DPU chips but are delivering the promised performance today.”

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