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| Microsoft Catapult | |
|---|---|
| Name | Microsoft Catapult |
| Developer | Microsoft Research |
| First release | 2014 |
| Type | Data center accelerator |
| Platform | Field-programmable gate array |
Microsoft Catapult is a project by Microsoft Research that integrates field-programmable gate arrays with datacenter servers to accelerate machine learning, search engine queries, and data center workloads. Catapult combines custom hardware with a tailored software stack to offload computation from central processing units and optimize latency and throughput for services such as Bing, Azure, and large-scale databases. The effort influenced subsequent accelerator initiatives across the technology industry and spurred collaboration between Microsoft and hardware vendors.
Catapult emerged as a research and engineering effort within Microsoft Research to address limitations of conventional x86 architecture servers when handling specialized workloads like deep learning inference and large-scale search advertising. The project uses field-programmable gate arrays developed by vendors such as Xilinx and integrates them into server infrastructure alongside Intel and AMD processors. Catapult served internal platforms including Bing, Office, and Azure services, and its deployment model informed designs at companies like Google, Amazon Web Services, and Facebook.
Catapult systems combine high-end FPGA boards with commodity server chassis built around processors from Intel and AMD. Early Catapult hardware used boards based on the Xilinx Virtex family and later integrated chips from Xilinx Versal lines. The architecture interconnects FPGA modules via PCI Express lanes and Ethernet fabrics, often paired with RDMA technologies such as InfiniBand and RoCE. Cooling and power design drew on best practices from hyperscale datacenter operators including Microsoft Azure, Google Data Center, and Facebook. The hardware supports offload patterns observed in accelerator projects like Google TPU, Intel Nervana, NVIDIA DGX, and programmable networking platforms like Barefoot Networks.
Catapult's software stack layers FPGA bitstreams with runtime systems that integrate into Microsoft's service infrastructure. Programming models included high-level synthesis flows using tools from Xilinx, hardware description languages like Verilog and VHDL, and higher-level frameworks inspired by domain-specific languages used at Google and Carnegie Mellon University. Catapult integrated with orchestration technologies such as Kubernetes-style schedulers and resource managers akin to Mesos and YARN. For machine learning workloads, Catapult-compatible toolchains connected to frameworks like Caffe, TensorFlow, and PyTorch, and interoperated with model formats like ONNX. Monitoring and debugging leveraged telemetry systems similar to Prometheus and tracing approaches from Dapper and Zipkin.
Catapult targeted reductions in latency and increases in throughput for services with stringent performance requirements. Notable use cases included accelerating search engine ranking and feature extraction for Bing, speeding ad-serving pipelines used by Microsoft Advertising, and offloading inference for conversational systems resembling Cortana and Microsoft Bot Framework. Benchmark comparisons referenced work on Google TPU and NVIDIA Tesla accelerators, showing favorable energy efficiency per query in scenarios with highly parallel, low-precision arithmetic. Real-world deployments demonstrated gains similar to those reported in academic venues such as International Conference on Computer Architecture and NeurIPS proceedings, influencing designs in cloud offerings like Azure Machine Learning.
The Catapult initiative began in the early 2010s as Microsoft Research explored hardware acceleration for web-scale services. Development combined research from labs that previously contributed to projects involving FPGAs at institutions like Stanford University and collaborations with industry partners including Xilinx and Intel. Key milestones encompassed prototype deployments within Microsoft's internal datacenters, publication of system papers at venues like ACM SIGCOMM and ISCA, and iterative hardware revisions paralleling advances by Xilinx and other vendors. The project timeline intersected with major industry shifts including the rise of deep learning research at University of Toronto and University of Montreal and commercialization efforts by startups such as Graphcore and Cerebras Systems.
Catapult influenced cloud architecture strategies and spurred competitors to develop specialized accelerators. Its integration model informed offerings from Amazon Web Services (with AWS Nitro and accelerators), Google Cloud (with Cloud TPU), and IBM hardware initiatives. Academic and industry research citing Catapult included groups at MIT, UC Berkeley, ETH Zurich, and Carnegie Mellon University, while ecosystem partners expanded FPGA application ecosystems that included HLS tool vendors and FPGA IP firms. The project helped legitimize FPGA-based acceleration for production services, shaping procurement and design choices at hyperscalers such as Microsoft Azure, Google, Amazon, and Facebook.