Artificial Intelligence Hardware and Software Co-Design and Neuromorphic Computing

Dear Colleagues,

This Special Issue aims to explore the rapidly evolving landscape of artificial intelligence (AI) through the lens of co-design methodologies that integrate hardware and software systems. As AI workloads become increasingly complex and data-intensive, the need for efficient, scalable, and domain-adapted computing paradigms has never been more urgent. Traditional interactions between hardware and software are proving to be inadequate for meeting the demands of next-generation AI applications, which range from edge intelligence and autonomous systems to large-scale generative models.

This Special Issue will focus on (a) the collaborative design of hardware and software systems that are optimized for AI performance, energy efficiency, and adaptability; (b) the role of emerging paradigms such as neuromorphic computing, which draws inspiration from the structure and functionality of the human brain; and (c) interdisciplinary research that bridges architecture, algorithms, circuits, and systems.

By gathering high-quality contributions from researchers across academia and industry, this Special Issue aims to highlight both foundational advances and practical implementations in areas such as AI accelerators, compiler and runtime support for custom AI hardware, spiking neural networks, and memory-centric designs. It also seeks to encourage discussion on benchmarking, design automation, and co-optimization strategies that holistically address the full AI system stack.

This Special Issue will complement and extend the existing literature in several ways. While prior works have often focused separately on hardware innovation or algorithmic development, this collection will emphasize the co-design principle, recognizing that optimal AI performance arises from the joint consideration of computational models, algorithm design, and underlying hardware. Moreover, by including neuromorphic computing, this Special Issue embraces biologically inspired approaches that offer promising paths for ultra-low-power and real-time processing.

This Special Issue will serve as a timely and valuable resource for researchers and practitioners interested in the co-evolution of AI hardware and software and in developing novel computing architectures that address emerging challenges in intelligent systems.

Dr. Yang (Cindy) Yi
Dr. Fangyang Shen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• AI hardware-software co-design
• neuromorphic computing
• spiking neural networks
• AI accelerators
• edge intelligence
• brain-inspired computing
• machine learning architecture
• hardware-aware AI models