AI hardware trends 2023 include the following:
Increased use of specialized AI chips: Companies are developing specialized chips that are specifically designed to handle the heavy computational demands of AI tasks. These chips, such as GPUs and TPUs, can significantly improve the performance of AI applications.
Edge computing: As AI becomes more pervasive, there is a growing need to process data and perform AI tasks at the edge, closer to where the data is being generated. This is driving the development of edge computing devices that can run AI algorithms locally, without the need for a connection to a central server.
Neuromorphic computing: Neuromorphic computing is an emerging field that aims to build computers that work more like the human brain. This involves using specialized hardware, such as memristors and spintronics, to create processors that can mimic the way neurons and synapses work.
Quantum computing: Quantum computing is a technology that uses the principles of quantum mechanics to perform certain types of computation that are not possible on classical computers. This technology is still in the early stages of development, but it holds the potential to revolutionize AI by enabling new types of algorithms and processing power.
IoT and 5G: The Internet of Things (IoT) and 5G networks are becoming more widespread, and are expected to drive the development of AI-enabled devices. This includes devices such as cameras, speakers, and appliances that can be controlled and monitored using AI.
Autonomous devices: The trend of autonomous devices such as drones, self-driving cars and robots are on the rise with the help of AI. These devices can sense their environment, and make decisions based on that information.
Overall, these trends are focused on making AI more efficient, powerful, and widely available by developing specialized hardware, moving AI tasks closer to the data, and exploring new types of computing technologies.