Artificial Intelligence (AI) is one of the leading concepts which will take the world into the next generation of interaction with machines and systems. Advanced algorithms that are built on deep neural network capability for self-learning are highly regarded in the market. Traditionally, advancements in computing hardware dictated innovations in software. However, with the advent of AI, investments in hardware to run deep-learning systems are rising. This is also because limitation of existing chips is being exposed to support AI.
Currently, a majority of the chips used for AI function utilizes the sophisticated Graphic card from leading market participant. The processor has several tiny computing devices running in parallel. However, these are not the designated AI chips. Several AI fabless start-ups spring up in the Silicon Valley and China in a race to design the processing chip with higher speed than existing graphical core chipsets.
Several start-ups are manufacturing chips for highly specific AI applications. However, if the product’s time-to-market does not match rapidly evolving demands of AI application, then the investment will run to risk. Among various applications available, machine learning, autonomous cars, virtual assistant, and process automation are witnessing the first wave of demand for AI application. This requires the processor’s computing power to be high, especially in learning applications. This is because, it takes a large amount of energy for a machine to adapt to its environment and learn the rules. However, execution does not require such powerful chip.
Adding to this, Internet of Things (IoT) is embedding more sensors and making the objects connected and smarter, which provides multiple growth opportunities for AI. For instance, the facial recognition software which is used for unlocking mobile devices has achieved its goal by adopting AI and deep learning techniques. The techniques are even applied to network optimization scenarios in communication service provider segment, where the software solution monitors the entire end-user network devices through sophisticated servers to quickly detect the problems and solve those pro-actively. Here data from the source are complexly computed by sophisticated processor to identify the situation and run corrective programs accordingly.
Several developments can be witnessed across logic and memory devices in terms of size reduction and form factor changes. Several memory market participants such as Intel, Micron, Samsung and other players are working on next generation memory and storage devices that will help to increase the data transaction speed. This will particularly benefit the data centers and cloud computing as they handle enormous amount of data.
The importance of data analytics lies in processing of Big Data to convert into highly valued insight. Such data have improvised performance of several businesses and even changed political scenarios in the past. The tremendous changes cannot be brought without the help of AI chips which processes information from several servers linked together.
Other areas where AI chips will have an impact include healthcare, energy, cybersecurity, and highly valued financial transactions.
Even more exciting application will be the self-driving and connected car projects. Several automotive companies are working toward getting self-driving cars on roads. In the process, it can be witnessed that semiconductor & automotive companies are extending their capabilities and jumping over to the chip or car market to develop their own self-driving cars. For example, Apple wants to build a self-driving car, while Tesla wants to build its own customized AI hardware. Automotive application, if proven, could be one of the major markets for AI chips, given the huge size of the transport market itself. AI chips will play a central role in the success of the self-driving cars in-order to swiftly adapt to its environment and make driving decisions within a fraction of seconds, keeping safety factor in consideration. AI chips will have to compute heavy amounts of data captured by systems such as LiDAR and other pictures or the video captured. The chips are expected to house several core GPU processors, along with accelerator chips for deep learning and computer vision accelerators.
Not only does the AI create value for highly priced chips, but the process equipment manufacturers will also witness surge in demand for equipment with new capabilities. Many leading equipment supplying companies acknowledge that AI will provide the next growth phase to the semiconductor industry. However, the question of impact that it will leave on the semiconductor industry will revolve around the cyclicality, or sustainability.
In conclusion, there are several applications that AI finds opportunity to play a central role, but the time-to-market will be a significant factor to ride on the wave. Assuming that start-ups will work their way up to meet that target, AI applications will definitely help to reduce the cyclicality of the semiconductor industry.
