Artificial intelligence is becoming a huge trend across the globe majorly due to generation and availability of massive digital content. The proliferation of mobile devices, easy availability of internet, and Internet of Things (IoT) devices are some of the primary factors responsible for this data explosion across industries and consumer segments. The advent of smart homes, smart workplaces, and smart cities concepts are further complementing the skyrocketing growth of artificial intelligence technology. In constantly changing digital world, information is key for companies in order to achieve sustainable growth in the fierce competitive scenario across industries. Companies are reaping great benefits due to digitization revolution and this digital shift is resulting into innovative revenue generation opportunities. All the industries in today’s world rely deeply on data analytics in order to gain useful consumer and business insights. These data insights help companies in reducing costs by improving operational efficiencies and thus enable them to remain competitive in the artificial intelligence chip market.
Business process automation is growing at a fast rate around the world and companies have been continuously automating their business processes in order to adapt with the fast changing digital world. With advancements in artificial intelligence (AI) technology and introduction of application-specific custom AI chips, businesses now have the capability to collect real-time data about their business processes and their customers. Artificial intelligence and data analytics allows businesses to transform this data into actionable insights. There are numerous use-cases of AI technology available in the market today and they are increasing at a fast pace across various industry verticals. The major applications of artificial intelligence include machine learning (ML), expert systems, natural language processing (NLP), automated speech recognition, AI planning, and computer vision. These applications of artificial intelligence allow businesses worldwide to improve their operational efficiency, lower operating costs, enhance service quality and customer experience. Major industries across are globe such as BFSI, retail, IT, automotive, telecom, healthcare, media & entertainment, government, and manufacturing are adopting and investing aggressively in disruptive technologies such as artificial intelligence, IoT, and predictive analytics. The interest from all these industries is mainly driven by the successful uses cases of AI in the banking, financial services, and insurance sector.
Artificial Intelligence: Major Applications and Prominent Industry Sectors
Source: The Insight Partners Analysis
With rising applications of artificial intelligence, data privacy and data security concerns are also increasing. Major reason for these concerns is the lack of sufficient data protection laws in most countries. The proper functioning and future growth of artificial intelligence is heavily dependent on the data collected from various sources. For this, businesses and customers have to allow access to their personal and confidential information to AI systems and devices. Increasing usage of digital services and data storage on the cloud has further raised data security and privacy concerns among consumers and businesses. To address these concerns, AI technology and solutions providers are developing systems/chips with inbuilt security features to boost customers’ confidence.
One of the major future trend evolving in the artificial intelligence chip marketplace is the application of AI chips in edge devices such as smartphones, drones, and self-driving cars. Most of the artificial intelligence processing which requires heavy computing is dominated by the cloud-based data centers. However, AI inference applications which are performed post-training are relatively less compute intensive. With the ever increasing AI applications, companies are taking notice of inference AI on edge devices. AI accelerators are evolving at an unprecedented rate and new hardware platforms are also being optimized to facilitate greater autonomy to edge devices such as mobiles, embedded, and internet of things (IoT) devices. Also with increasing concerns over data privacy and data sharing, the trend is shifting towards devices from data centers in order to keep data constrained to specific devices. Various companies and startups are focusing on moving the inference part of the AI workflow to the devices. Based on the type of AI application and device category, there are multiple hardware options such as CPUs, GPUs, ASICs, FPGAs, and SoC accelerators to perform AI edge processing. The proliferation of smartphone-embedded AI processors by tech giants such as Apple, Samsung, and Google is gaining traction and driving the adoption of AI chips in edge devices.
AI robotics is another noteworthy application of artificial intelligence which will drive the penetration of AI chips in future applications such as self-driving cars, drones, smart appliances, and industrial IoT. Tech giants such as NVIDIA, Apple, Google, Huawei, and Intel are showing huge interest and investing heavily in edge inferencing applications of artificial intelligence chips. With these developments, it is expected that edge applications are going to be the future of artificial intelligence in the coming years.
Another fast evolving trend in the artificial intelligence chip industry is the development of Application-specific integrated circuits (ASICs) for specific industry applications. ASICs are task specific and less flexible, but they have superior performance in comparison to other available AI chips. These custom AI chipset architectures offer a diverse range of AI applications including machine learning, deep learning, and natural language processing among others for both training and inference. To address the increasing demand and variety of AI workloads, vendors are now mixing a wide range of chip technologies in their products. In terms of efficiency also, ASICs are more efficient in performance/dollar and performance/watt terms. There are multiple companies including Microsoft, Google, Amazon, Apple, and Tesla that are working on the development of some type of ASIC chips for artificial intelligence applications. The future of AI chips lies in these custom and application-specific ASICs. These custom AI chipset architectures offer a diverse range of AI applications including machine learning, deep learning, and natural language processing among others for both training and inference applications.
There are a huge number of innovative and well-funded startups entering the artificial intelligence market landscape. Few key startups disrupting the AI chip market include Graphcore, Cambricon, Thinkforce, Barefoot Networks, Unisound, Horizon Robotics, WestWell Labs, Brain Corp, DeePhi Tech, Kneron, and IntelliFusion among many others. These companies are working on a variety of products related to artificial intelligence, some of them are mentioned in the table below.
Artificial Intelligence: Key Startups and their Area of Work
Source: News articles, The Insight Partners Analysis
Some of the notable developments in the start-up landscape include, Graphcore working in order to lower the cost of accelerating AI applications and to increase performance by up to 100x compared to today’s systems; WestWell Lab’s DeepSouth neural processors (ASIC) which simulate human brain neurons; and Horizon Robotics’s ASICs for face recognition and video analytics solutions in smart cameras, self-driving cars and others. These tech-savvy, robust and ‘think-big’ startups are making the AI market more competitive which is leading to more innovations and technology improvements. With all the technological advancements happening in the field of artificial intelligence and investments made by big tech companies, artificial intelligence technology holds a promising future in the coming years.
Monika is a Research Analyst in the premier market research and strategy consulting firm, The Insight Partners. Her expertise lies in the Technology, Media and Telecommunications domain. She is an Electronics and Communication Engineer with MBA in Marketing & Finance. She has experience in Market Research, Report Writing, Business and Strategy Consulting. Artificial Intelligence, Blockchain, and Internet of Things are some of her interest areas.