Is ASIC going to win the Artificial Intelligence race?
An application-specific integrated circuit (ASIC) is a microchip designed for specific applications; for instance, any kind of transmission protocol or a hand-held computer. ASICs are general integrated circuits similar to microprocessors and random-access memory chips, which are incorporated in the PCs. ASICs are used for various purposes, including auto emission control, environmental monitoring, and personal digital assistants (PDAs).
An ASIC is a graphics processing unit (GPU) used for processing graphics algorithms. The core difference between these two is that an ASIC offers an instruction set and libraries, which enable the GPU to be programmed and operated on locally-stored data as an accelerator for numerous parallel algorithms.
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In integrated circuit (IC) technology, it is a common procedure that certain tasks are done initially on general-purpose processors followed by field programmable gate arrays (FPGAs) and then custom ASICs. In comparison to other chips, ASICs deliver the best performance, power, and efficiency. However, designing an ASIC chip and getting it to the point of manufacturability may take tens of millions of dollars. Hence, ASICs are mostly used when a market application has reached a certain level at which the advantages of the ASIC solution become compelling.
How are ASIC chips incorporated in AI?
With respect to machine learning and artificial neural networks, several ASICs have become vital components in and after 2018. An example of an ASIC designed for machine learning is the tensor processing unit (TPU). Google has developed a series of ASICs for machine learning called TPUs, which are optimized sources to run open-source machine learning software, TensorFlow that is also developed by Google. In addition, Fujitsu has been planning to launch its chip called the deep learning unit (DLU). Similarly, Bitmain, one of the leaders in cryptocurrency mining, uses the ASIC chips to produce powerful and efficient cryptocurrency miners.
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Artificial intelligence (AI) has been really heating up these days. Although, the technology has been around for quite some time, but lately, it is getting attention for applications, such as data center analytics, autonomous vehicles, and augmented reality. In the future, AI will be everywhere, be it cars, camera servers, or back ends. An analogy is replacement of a horse with a motor vehicle, which resulted in big-time changes and altered the world dramatically.
What is the current scenario of super 7 companies?
Google crowns itself as an “AI First” company. The AI technology has been in use for some time now and has its role across the entire business, such as search engines, self-driving vehicles, Google cloud, Google home, and several other new and existing products and services. Hence, Google built and took control of its own hardware TPU accelerators and software framework, TensorFlow, on which they build their products and services.
Among the other super 7 members, mainly, Amazon, Alibaba, Baidu, Facebook, Microsoft, and Tencent, it is highly possible that they build their own accelerator, but none of them exhibits the attributes as Google does. However, rest of these companies are close to Google in introducing their own chips, but each has been moving in a different direction.
Baidu has publicly released that it has partnered with NVIDIA for AI initiatives in cloud, home, and autos. Having stated that, it does not mean Baidu cannot create its own chip, but in the current scenario, it concentrates more on its software and services.
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Microsoft, on the other hand, is the second-largest cloud services provider and consists of more than 5,000 AI engineers. It has been looking forward to dominating AI with the help of its tools and APIs for enterprise customers. However, Microsoft uses Altera FPGAs from Intel in its Azure and Bing infrastructure because the company opines that it gains from a more flexible hardware platform in a fast-changing world. In addition, the company also uses NVIDIA GPUs for training its neural networks.
Lastly, Amazon seems to be the closest to Google because Amazon web services (AWS) is huge, and the organization has been increasingly investing in AI. Although, Amazon prefers the Apache MXNet framework for development of AI, its AWS cloud services for AI support all the major frameworks, making it the open software of AI development. Amazon had previously entered into the ASIC race by acquiring Ana Purna labs in 2012, to remove the costs and latencies required in the AWS infrastructure. However, Amazon prefers to be keen on using FPGAs, owing to their all-programmable nature.
According to a recent report published by Allied Market Research, titled, AI Chip Market by Technology and Application: Global Opportunity Analysis and Industry Forecast, 2017–2023, the global AI chip market was valued at $661 million in 2016, and is projected to reach around $11 billion by 2023, growing at a CAGR of 49.6% from 2017 to 2023.
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