A recent business intelligence publication by Transparency Market Research (TMR) notifies that while startups are mushrooming in the global deep learning chipset market, the competitive landscape remains moderately consolidated among a few players. The analysts of the report have identified NVIDIA Corporation and INTEL Corporation as the top two companies who were ahead of the curve in 2016, whereas Google Inc., Qualcomm Incorporated, IBM Corporation, CEVA Inc., Xilinx, Graphcore, Teradeep Inc., and Advanced Micro Devices are some of the other notable companies currently harnessing the prospects of deep learning.
Since the entry barriers of this market are not incredibly high and heavily dependent on technologies that are not patented, startups have a strong chance to make a mark. In the near future, the shares in the global deep learning chipset are expected to head towards a fragmented scenario, although most of the prominent chip manufacturers are anticipated to hold onto their position of strength. Market leaders are expected to frequently indulge into acquisition and collaboration activities with startups with niche concepts. For instance, in January 2016, Google Inc. agreed to collaborate with Movidius to enhance its deep learning portfolio on mobile devices, which was later acquired by Intel Corporation. Later in August the same year, Intel Corporation also acquired Nervana Systems in order to further solidify its position in developing the hardware chipsets platform.
If the projections of the TMR report are to be believed, the demand in the global deep learning chipset market will increment at a phenomenal CAGR of 24.7% during the forecast period of 2017 to 2025. The report has estimated that from its calculated worth of merely US$150.17 mn in 2015, the opportunities in the global deep learning market will translate into a revenue of US$1,264.78 mn by the end of 2025.
Based on type, the TMR report segments the global deep learning chipset market into application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), field programmable gate arrays (FPGAs), and others. Compute capacity bifurcation of the market has been done into low (less than 1 TFlops) and high (greater than 1 TFlops). End-user classification of the market has been done into automotive, consumer electronics, healthcare, industrial, aerospace and defense, and others. Geographically, the analysts have detected that although North America currently provides for the maximum chunk of demand, the region of Asia Pacific will overtake in terms of demand and revenue by the end of 2025.
The global deep learning chipset market is primarily driven by significant improvements in the chip algorithms in the last few year, which has enormously increased their ability to consume and manage data efficiently. With increasing adoption of the Internet of Things (IoT) that promises to connect billions of new devices and pertaining data streams, it is very likely that much more digital data will be generated and consequently, the industry of machine learning and deep learning will flourish. Moreover, newly build algorithms are shifting the learning from one particular application to another application, making it easily possible for the machines to learn from very fewer examples. Invention of a related computer chip, which is known as GPU or graphic processing unit, is turning out to be very meaningful and effective when it is being applied to types of calculations required for neural nets. Speed up of 10x times are also very much viable when the neural nets will move from traditional CPU to GPU.