The capability of AI applications in processing and storing massive amounts of data is impacting semiconductor design and production.
FREMONT, CA: From big data analytics and military equipment to facial recognition software and self-driving cars, artificial intelligence (AI) plays a huge role. It is also impacting the semiconductor industry, bringing new challenges and opportunities every day. AI defines a machine or software application’s ability to learn, reason, and perform similarly to human cognition.
Recent advances in AI technology have seen a revolution in the field with the development of machine learning algorithms capable of processing massive amounts of data, opening new possibilities for AI devices. Currently, AI applications can not just process data but can learn from experience and apply that learning to improve functions.
With AI applications gaining momentum in the industrial, retail, health care, research, military, and consumer sectors, there is an increased demand for specialised sensors, integrated circuits, enhanced processors, and improved memory. This necessity changes the semiconductor supply chain by explicitly impacting design and manufacturing decisions.
AI demands will have lasting impacts on semiconductor design and will have radical consequences on semiconductor design and production due to the massive volume of data processed and stored by AI applications. Semiconductor architectural improvements need to address data use in AI-integrated circuits. This improvement refers to accelerating the movement of data in and out of memory with increased power and more efficient memory systems while also enhancing overall performance. An option is to design chips for AI neural networks that perform like human brain synapses. Such chips will power and send data only when required instead of constant signals.
Nonvolatile memory will also see more use in AI-related semiconductor designs as it can hold saved data without power. Integrating nonvolatile memory on chips with processing logic will make systems on chip operators possible, thereby meeting the demands of AI algorithms.
Semiconductor design improvements are materialising to meet the data demands of AI applications but pose potential production challenges. The need for memory has enlarged AI chips, which hinders chip vendors' ability to make money while working on specialised hardware. Moreover, it is also expensive to manufacture a specialised AI chip for every application.
A general-purpose AI platform has the key to addressing this challenge. System and chip vendors can augment the general-purpose platform with accelerators, sensors, and inputs and outputs. This allows manufacturers to tailor the platform for different workload requirements of any application while still having a cost-effective platform. Facilitating the rapid evolution of an application ecosystem is an additional benefit of a general-purpose AI platform.
From a production standpoint, the semiconductor industry will hugely benefit from AI adoption. AI will be ubiquitous in all process points, providing the data needed to alleviate material losses, enhance production efficiency, and reduce production times.