Here at The Next Platform, we tend to keep a close eye on how the major hyperscalers evolve their infrastructure to support massive scale and evermore complex workloads. Not so long ago the core services were relatively standard transactions and operations, but with the addition of training and inferencing against complex deep learning models--something that requires a two-handed approach to hardware--the hyperscale hardware stack has had to quicken its step to keep pace with the new performance and efficiency demands of machine learning at scale. While not innovating on the custom hardware side quite the same way as Google, Facebook has shared some notable progress in fine-tuning its own datacenters. From its unique split network backbone, neural network-based viz system, to large-scale upgrades to its server farms and its work honing GPU use, there is plenty to focus on infrastructure-wise. For us, one of the more prescient developments from Facebook is its own server designs which now serve over 2 billion accounts as of the end of 2017, specifically its latest GPU-packed Open Compute based approach.

Facebook on Wednesday rolled out some staggering statistics related to its social networks. Each day, users watch 100 million hours of video, 400 million people use Messenger, and more than 95 million photos and videos are posted on Instagram. That puts a heavy load on Facebook's servers in data centers, which help orchestrate all these services to ensure timely responses. In addition, Facebook's servers use machine learning technologies to improve services, with one visible example being image recognition. The story is similar for Microsoft, which is continually looking to balance the load on its servers. For example, Microsoft's data centers apply machine learning for natural language services like Cortana.

AMD, Dell EMC, Google, Hewlett Packard Enterprise, IBM, Mellanox Technologies, Micron, NVIDIA and Xilinx announced a new, open specification that can increase datacenter server performance by up to 10 times. The new standard is called Open Coherent Accelerator Processor Interface (OpenCAPI). It is an open consortium to provide a high bandwidth, low latency open interface design specification. It will enable corporate and cloud data centers to speed up big data, machine learning, analytics, and other emerging workloads. Servers and related products based on the new standard are expected in the second half of 2017. Capable of 25Gbits per second data rate, OpenCAPI outperforms the current PCIe specification which offers a maximum data transfer rate of 16Gbits per second.

For years, Microsoft has been delivering speedy and accurate Bing results with experimental servers called Project Catapult, which have now received an architectural upgrade. The Catapult servers use reprogrammable chips called FPGAs (field programmable gate arrays), which are central to delivering better Bing results. FPGAs can quickly score, filter, rank, and measure the relevancy of text and image queries on Bing. Microsoft has now redesigned the original Catapult server, which is used to investigate the role of FPGAs in speeding up servers. The proposed Catapult v2 design is more flexible in circumventing traditional data-center structures for machine learning and expands the role of FPGAs as accelerators.

Facebook is releasing for free the designs of a powerful new computer server it crafted to put more power behind artificial-intelligence software. Serkan Piantino, an engineering director in Facebook's AI Research group, says the new servers are twice as fast as those Facebook used before. "We will discover more things in machine learning and AI as a result," he says. The social network's giveaway is the latest in a recent flurry of announcements by tech giants that are open-sourcing artificial-intelligence technology, which is becoming vital to consumer and business-computing services. Opening up the technology is seen as a way to accelerate progress in the broader field, while also helping tech companies to boost their reputations and make key hires.