Artificial intelligence (AI) technology can no longer be considered novel stuff. The past couple of decades have witnessed a virtual explosion of AI use cases wherein businesses across industries implement AI technology to accelerate their data-intensive processes. Business applications that utilize machine learning (ML) methods notably deep learning to make processes faster, smarter, and more efficient are proliferating at a rapid pace. Data-empowered companies are increasingly leveraging AI algorithms and ML models to power next-gen robotics, sensors, and IoT systems.
Deep learning and neural networking models will continue to ensnare consumers and businesses alike for the next several years. From simple tasks in tablets and phones to complex ones such as image recognition and natural language processing in autonomous vehicles, companies are throwing their weight behind the diffusion of AI technology. AI-driven systems have become a transformative framework for multitude of consumer products such as autonomous cars, drones, smart cameras, and mobile devices.
AI-Optimized Chipsets to Enable Deployment of AI at Scale
Deployment of AI comes at scale is yet to be realized. Hardware forms a crucial backbone to develop, train, and implement AI algorithms. General purpose chips notably CPUs lag behind meeting the performance and technical requirements of high-capacity AI services.
As AI algorithms and data science techniques become entrenched in decision making of forward-looking businesses, requirements of hardware have changed drastically over the years. Subsequently, AI chipsets—part of processors and the hardware optimized to manage computation-heavy tasks—elicit enormous attention among semiconductor companies testing their expertise in various stages–design, fabrication, assembly, and packaging. A clutch of companies have joined the ecosystem in specific stages in a bid to capture the market value in the AI chipsets market. Intel and Samsung have emerged as integrated device manufacturers while Broadcom and Qualcomm are becoming popular as fabless companies that specialize in the design of AI chips and outsource the manufacture to third parties are enriching the ecosystem. Demand for novel designs in AI present significant revenue streams to fabless companies and will encourage investments in the artificial intelligence chipsets market.
Enterprise-scale AI Hardware that Can Train Neural Network Models
Tremendous technological developments in AI-optimized chipsets have shaped the contours of the artificial intelligence chipsets market. Field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) are prominent artificial chipsets being deployed to meet different objectives of training, drawing inference, and eventually refining the AI algorithms.
Stalwarts as well startups are testing the water by developing and commercializing deep learning chips. FPGAs are popularly utilized to draw inferences in AI applications that deal with real-world data inputs and hence demonstrate flexibility for a broad range of AI applications. On the other hand, ASICs have become more suitable for specific applications. The ASIC by IBM (AIU) has gathered immense steam among deep-learning AI applications, for instance.
Yet another category that is witnessing intense R&D spending is edge AI chip, where the processing of AI is done on the edge rather than on the cloud. Edge AI chipsets are likely to gain adoption due to preference of chipsets that need low power and fast processing of data such as in miniaturized devices. Edge AI-optimized chips also bring a unique advantage—privacy of data, since processing of data occurs locally instead of remote servers.
Investments pouring from every quarter are likely to fuel growth of the AI chipsets market. The American technology company Nvidia commands a significant clout in the market. Its A100 has generated intense interest among customers in biotech, finance, and manufacturing sector who want to race ahead in the race to adopt AI applications. It could be used to train or refine machine learning models of now-famous tools ChatGPT and Bing AI.
Perhaps, expertise of the U.S. chipmaker in general purpose AI chips, graphic processors, (GPU), type of AI chipset, accounts for the prominence. Burgeoning usage of AI chipsets in deep learning applications is poised to offer Nvidia a distinct advantage over Intel, opine experts. This is not to say that the dominance of the behemoth is uncontested. A clutch of startups has emerged to disrupt the landscape.
An Israel-based AI chip maker Hailo is a new entrant who plans to challenge well-entrenched chip manufacturers in the artificial intelligence chipsets market. Equipped with US$ 224 Mn funding since its last Series C funding, the company has grown their R&D spending on AI chips to cater for deep learning applications. These will accelerate the deployment of AI in a range of consumer devices including smart home appliances and autonomous vehicles.
Small-sized companies for now seem to stay away from GPUs to support enterprise-scale AI hardware. But many are keen on developing and unveiling AI chips for computer vision and chatbot AI applications. Rebellions Inc., a South Korean startup, hopes to offer AI chipsets for data centers to tap into the domestic demand. Its R&D estimated at $800 million over the next five years is backed by the South Korean government. However, the drive for commercialization of AI chipsets by startups is yet to gain momentum. Hardware for deep learning will continue to attract multi-million-dollar investments, shaping the future of the artificial intelligence chipsets market industry. In 2021, the market stood at US$ 45.5 Bn. The market size is projected to expand at a CAGR of 31.8% during the forecast period of 2022 – 2031.
Rise of Generative AI: Makers of Memory Chips Capture Lucrative Business Opportunities
Generative AI applications have recently built up a lot of hype among consumers. ChatGPT is a brilliant example here. OpenAI, a San Francisco-based startup, which is behind this viral chatbot has disrupted the generative AI space. Funded by Microsoft, the company is working to customize AI chips to fulfil the requirement of chatbots. An example is usage of the technology is Snapchat. The social media platform is using OpenAI’s GPT technology for its chatbot My AI. Memory chipmakers witness an incredible potential here. Surge in popularity of AI-optimized chipsets for neural network models among businesses will broaden the canvas for companies in the global artificial intelligence chipsets market.
Smartphone and smart consumer device manufacturers eye a significant slice of the market. Heavyweights such as Apple and Sony are placing huge bets on AI-optimized chipset technologies. Apple for one has claimed that its A11 and A12 Bionic chips, 64-bit ARM-based system on a chip (SoC), are likely to accelerate the CPU performance in next-gen mobile phones. SoCs evoke significant attention among semiconductor companies. Novel approaches in system on chip (SoC) designs are likely to unlock incredible avenues for players in the artificial intelligence chipsets market.
Contributed by Baibhav Anand has been active in developing and publishing content for more than eight years now. He has a knack to simplify complex technical concepts and ideas in a wide range of industries into easy-to-consume content. He has been working for Transparency Market Research for over six years and is actively engaged in various content marketing initiatives from end-to-end. He leverages his content marketing skills and business acumen to drive user engagement. Each of the articles is aimed to inform, educate, engage, and persuade readers on multiple fronts. The current write-up on AI chipset market closely follows recent market developments and scrutinizes how product innovations are likely to shape the future industry landscape.