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Friday, November 08, 2019

Why End-to-End AI Hardware Solutions are Disruptive

How can IT vendors and cloud service providers differentiate their artificial intelligence (AI) offering, when the solutions tend to have the same basic capabilities? The answer could be new semiconductor chipsets that disrupt the status quo.

During the last two years, several hyperscale cloud service providers -- including Alibaba, Amazon, Facebook, Google, Huawei, and Tencent -- have been busy designing their own in-house chipsets for handling AI workloads in their data centers.

According to the latest worldwide market study by ABI Research, cloud service providers commanded a 3.3 percent market share of the total AI Cloud chip shipments in the first half of 2019.

AI Chipset Market Development

These players will increasingly rely on their own in-house AI chips and will be producing a total of 300,000 cloud computing AI chips by 2024, representing 18 percent of the global cloud AI chipsets shipped in 2024.

The requirements for intelligent services by many enterprise verticals are also pushing cloud service providers to upgrade their data centers with AI capabilities, which has already created more demand for cloud AI chipsets in recent years.

ABI Research has forecast that revenues from these chipset shipments will increase significantly in the next five years -- from $4.2 billion in 2019 to $10 billion in 2024.

Established chipset suppliers such as NVIDIA, Intel, and, to a certain extent, Xilinx will continue to dominate the market, thanks to the robust developer ecosystem they've created around their AI chipsets.

However, these vendors will increasingly face intensive competition from many new entrants and challengers, particularly their clients, namely the hyperscale global cloud service providers.

"The approach by webscale companies to develop in-house AI chips allows for better hardware-software integration and resources tailored to handle specific AI networks, which serves as a key differentiating point not only at the chipset level but also at the cloud AI service level," said Lian Jye Su, principal analyst at ABI Research.

This trend, initiated by Google in 2017, has led to many other webscale companies to follow Google’s track. Baidu immediately followed with its own AI chipset, Kunlun, in 2018, and later in the same year, Amazon introduced its Inferentia chip to support its Amazon Web Service (AWS).

According to the ABI assessment, Huawei is another captive company that has made a move toward using its in-house chips for its cloud services in an attempt to reduce its reliance on Western chipset suppliers. The company launched Ascend 310 and 910 in 2018 and has since expanded its product lineup into a series of cloud AI hardware, including an AI accelerator card and AI system.

Recently, Huawei launched Atlas 900, an AI training cluster which is a direct competitor to NVIDIA’s DGX and features over 1,000 Ascend 910 chipsets.

Outlook for AI Chipset Competition Growth

"This further expands the footprint of cloud AI service providers, as they are also competing with Intel and NVIDIA for the mindshare of developers. By offering end-to-end AI hardware solutions, Google, Amazon, and Huawei can ensure that their users will enjoy the ease of development and deployment while creating an active and vibrant developer community around their chipset solutions and ultimately generating a large user base for their cloud AI services," concluded Su.

However, as more enterprise CIOs and CTOs seek ways to gain a competitive edge via their artificial intelligence deployment strategies, we'll likely see a corresponding shift away from platform supremacy, and more emphasis on practical AI applications that enable the user's desired business outcomes. Let's not forget, enterprise developers focus on the needs of line-of-business leaders.