Why are so many companies suddenly jumping into the datacenter accelerator game? Major chip companies such as Intel, NVIDIA and Xilinx as well as startups such as Nervana (being acquired by Intel), Wave Computing, GraphCore, KnuPath and others are all vying for a piece of a rapidly growing market. That market consists primarily of just seven customers, the world’s largest datacenters: Alibaba, Amazon.com, Baidu, Facebook, Google, Microsoft and Tencent. These companies are increasingly turning to technologies that can run specific algorithms at least 10 times faster in order to meet the demand for applications such as machine learning, ultra-high-definition video streaming and complex data analytics. While GPUs (Graphics Processing Units) from NVIDIA have been leading much of this trend, Field Programmable Gate Arrays (FPGAs) hope to now contend to become a major player. (Recall that Intel invested in this market through their $16.7B acquisition of Altera last year.) Now Xilinx is aiming to take this market mainstream with a new offering that speeds development of these reprogrammable acceleration chips. FPGAs are used today for processing of automotive sensor data, network accelerators, embedded industrial applications and other tasks where high performance and energy efficiency are required but where the volumes do not cost-justify developing a custom Application-Specific Integrate Circuit (ASIC) chip. Microsoft recently discussed that they use FPGAs in practically every server in their massive datacenters. So why doesn’t everyone use these esoteric chips in their datacenter? Unfortunately, as it turns out, FPGAs are notoriously difficult to program, requiring hardware (that’s the “Gate” part in FPGA) as well as software expertise–a rare combination of talents. This is in fact the challenge that Xilinx is hoping to address by delivering common building blocks and tools for 3 key hyperscale workloads that demand more performance and that are still in the relatively early stages of algorithm development. What has Xilinx announced? Xilinx believes that the trend described above is in the early stages and, by lowering the programming hurdles and easing development of FPGAs, that these reprogrammable hardware devices will go “mainstream” in hyperscale datacenters. So, Xilinx has pulled together a suite of software, tools and hardware reference designs to accelerate, well, acceleration. Xilinx is targeting three of the fastest growing workloads that demand more performance today: machine learning, video transcoding for live 4K streaming and SQL queries for data analytics. The Xilinx Reconfigurable Acceleration Stack is targeting the markets for machine learning, video transcoding and SQL queries for data analytics. (Source: Xilinx.) Why machine learning, video & SQL acceleration? Xiinx is focusing on the workloads that have large and fast-growing footprints in hyperscale datacenters. Machine Learning has been all over the news lately as AI becomes the new big thing, while SQL analytics have become pervasive across hyperscale applications. And live High-Dev (4K) video streaming of sporting events and gaming competition is becoming big business around the globe. All three of these workloads demand far more performance than a CPU-only infrastructure can deliver, and are well suited for FPGA acceleration. Xilinx has shared benchmarks that demonstrate acceleration from 4 to over 25 times the performance of a CPU-only server across the spectrum of these applications. Perhaps most importantly, these three workloads represent significant revenue opportunities, not just cost savings in IT, for the Super Seven companies. Will this drive FPGAs into the mainstream? FPGAs have long held promise for a wide swath of workload acceleration in large datacenters but have been held back by the fear, real or imagined, of the programming difficulty and lack of available skills. It is too early to tell if the building blocks Xilinx has launched will close this gap, but the timing of the launch on the heels of Microsoft and Baidu’s recent announcements could not have been better. So, I would turn the question around, and state that if FPGAs are going to go mainstream, then it will take something like these acceleration stacks to break down the barriers to volume deployment. And if Microsoft is any indication, we are likely to see more, not less, adoption of FPGAs in the world’s largest datacenters. For more detailed information regarding Xilinx’s Reconfigurable Acceleration Stack, including benchmarks, please see the recently published Moor Insights & Strategy research paper on this topic here.