Deep Learning Applications to Drive Global Microserver Industry

Microservers are a system-on-chip device featuring multiple single-socket servers sharing cooling fans, power supply, chassis, and other such hardware. This common platform allows microprocessors to achieve much higher power efficiency than conventional servers, since more tasks can be carried out on the same volume of power. Microservers were developed in order to integrate server motherboard tasks on to an easily portable and power-efficient unit. This eliminates the need for support chips complementing the server function, which has resulted in the power efficient and space-saving design of microservers.

According to Transparency Market Research, the global microservers market was valued at more than US$1 bn in 2012. Due to the rapidly increasing adoption of microservers in diverse industries, the market is expected to exhibit a stellar 43.4% CAGR from 2013 to 2019, with the market’s valuation expected to rise to US$30.2 bn over the period.

How will deep learning applications drive the global microservers market?

Microservers used in graphics processing units (GPUs) have undergone significant technological advancement over the last decade, as the flourishing video game industry has provided a stable source of funding. Now, however, microprocessors from GPU specialists such as Nvidia are now being used to power machine learning and artificial intelligence programs. This is due to the ability of microprocessors to carry out multiple functions at the same time. This remains a crucial aspect of any prospective AI program, with the interconnection between the multiple units on a microserver simulating the dynamic nervous activity in the human brain.

Conventional servers are an excellent option for intensive computing, but fall woefully short of microservers when it comes to multitasking. In October 2015, Google DeepMind’s AlphaGo program beat the European Go champion, a game considered extraordinarily difficult for artificial intelligence to grasp due to the wide range of possibilities in it. In March this year, the program beat one of the top ranked Go players in the world, Lee Sedol, marking a historic achievement and a crucial milestone for artificial intelligence. The increasing interest in artificial intelligence is thus a major driver for the global microservers market.

This is not the first use of Nvidia’s GPUs for AI applications – in 2012, the University of Toronto built an image classification system using Nvidia GPUs. The support provided by Nvidia to a programming language called CUDA, which allows end users to redirect the GPU’s capacity towards non-graphics applications, is crucial in this endeavor. Similarly, these GPUs can be used in speech recognition programs, where ‘learning’ is absolutely vital, and other such applications.

What are some of the major restraints on the microservers market?

The major restraints on the global microservers market are the high cost of switching to microservers and the compatibility issues between conventional platforms and advanced microservers. However, the increasing demand for application-specific, low-power servers is likely to ameliorate both these issues, as major companies requiring application-specific servers have the financial capacity to restructure the conventional platforms to fit the advent of microservers. This will also help reduce the overall costs related to microservers, thus helping the global microserver industry gain demand from progressively smaller companies.


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