A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications

Over the last decade, artificial intelligence has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, 3D integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefits the designs in the future.

💡 Research Summary

This paper explores the importance of 3D integrated system design in the fields of artificial intelligence and neuromorphic computing. Over the past decade, AI has found numerous applications across society, leading to more sophisticated solutions and a growing number of use cases that have highlighted the need for addressing performance and energy efficiency challenges during implementation. To tackle these issues, there is increasing interest in neuromorphic chips. Neuromorphic computing relies on non-Von Neumann architectures as well as novel devices, circuits, and manufacturing technologies to mimic human brain functions.

3D integration plays a crucial role in AI hardware by enhancing performance and energy efficiency while maintaining the continuity of scaling laws. This paper reviews the unique opportunities that 3D integration provides for neuromorphic chip design, discusses emerging opportunities in next-generation neuromorphic architectures, and examines existing obstacles. Neuromorphic architectures, which are inspired by the brain’s functionality and structure, face significant challenges due to limited understanding of these aspects.

Despite these challenges, substantial investments continue to be made towards developing neuromorphic chips. The paper argues that 3D integration not only offers strategic advantages for cost-effective and flexible design but also provides future flexibility in incorporating advanced capabilities into designs. This could further benefit the development of more efficient and effective AI hardware solutions.