Year: 2024
Type: article
Abstract: The rapid rise of artificial intelligence (AI) is revolutionizing industries such as healthcare, robotics, and automation, creating a demand for more efficient, adaptive computing systems.In recognition of AI's profound impact, the 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton for their pioneering work in machine learning and artificial neural networks.[1][2][3] This underscores the growing recognition within the scientific community of AI's transformative role in society.However, the energy-intensive nature of deep learning models and the computational demands of modern AI continue to outpace the capabilities of traditional silicon-based hardware, posing significant sustainability challenges as AI scales.[4,5] To address these growing challenges, there is an increasing need for hardware that can meet the demands of next-generation AI.The concept of "more-than-more" calls for breakthroughs beyond traditional improvements, such as new materials and innovative architectures.Neuromorphic computing, inspired by the brain, offers a promising direction, providing biologicallevel energy efficiency and adaptability, making it ideal for nextgeneration AI hardware.[6][7][8][9]
Source: Advanced Electronic Materials
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,100
OpenAlex ID: W4405123844