Frontiers in Science Lead Article
Published on 16 Dec 2025
Breaking the memory wall: next-generation artificial intelligence hardware
Frontiers in Science
doi 10.3389/fsci.2025.1611658
- 85,356 views
- 4 citations
Lead article
Expert insights
ARTICLE EVENT
Discussion with the article authors
Prof Kaushik Roy and colleagues delved into neuromorphic computing at a virtual Frontiers Forum Deep Dive session on 12 February 2026.
Frontiers in Science Editorial
Published on 16 Dec 2025
Why does artificial intelligence need active memory to succeed?
Prof R. Stanley Williams, Texas A&M University, USA—AI’s unsustainable energy and infrastructure requirements demand unified, brain-inspired architectures that combine memory and computation to eliminate costly data-movement bottlenecks unresolvable by software-level advances alone.
POLICY OUTLOOK
Published on 16 Dec 2025
Designing capability: institutions and the future of emerging compute
Dr Vilas Dhar, Patrick J. McGovern Foundation, USA—During this brief technological transition, policymakers must embed equitable access and accountable governance into AI, advancing the global public good rather than entrenching current political and economic inequalities.
Key points
Efficient artificial intelligence (AI) hardware is crucial for resource-constrained applications such as healthcare and transportation, where it enhances performance, reduces costs, and supports real-time decision-making.
Overcoming the memory wall in traditional hardware is critical for enhancing AI computational efficiency, reducing latency, and enabling faster, more effective processing of complex algorithms.
Compute-in-memory (CIM) paradigms using different memory technologies, such as embedded non-volatile memory (eNVM), static random-access memory (SRAM), dynamic random-access memory (DRAM), and flash memory, help develop energy-efficient AI hardware by tackling the memory wall problem.
Stochasticity in AI algorithms (e.g., via spike timing-dependent plasticity or STDP) and hardware (e.g., via spin–orbit transfer magnetic tunnel junctions or SOT-MTJs) can be leveraged to improve energy efficiency for diverse workloads and could unlock novel capabilities.
Co-designing hardware and algorithms to optimize energy, latency, and accuracy will lead to the development of a “converged platform” for artificial neural networks (ANNs) and spiking neural networks (SNNs), suitable for diverse AI applications.
For all readers
EXPLAINER
Published on 16 Dec 2025
Over the memory wall: how new chips could unlock faster, greener AI
A summary of the lead article in a Q&A format, with a figure and video.
Frontiers for Young Minds
Published on 16 Dec 2025
Wired for the Future: How AI Hardware is Changing the Tech World
A version of the lead article written for—and peer reviewed by—kids aged 8-15 years.
News
PRESS RELEASE
Published on 16 Dec 2025
How breaking the ‘memory wall’ using brain-inspired algorithms could help overcome AI energy costs
AI hardware needs to become more brain-like to meet the growing energy demands of real-world applications, according to researchers. (Photos: Prof Kaushik Roy, Dr Adarsh Kosta, and Dr Tanvi Sharma, Purdue University, USA).
PRESS RELEASE
Published on 21 Jan 2026
Rethinking AI hardware to meet growing energy demands
As AI models scale rapidly, energy efficiency has become a critical challenge. A new Frontiers in Science lead article outlines brain-inspired hardware approaches designed to break today’s computing bottlenecks.
NEWS STORY
Published on 18 Dec 2025
Brain-inspired algorithms could dramatically cut AI energy use
A new study suggests a novel way to build computer architecture for AI models using brain-inspired algorithms.
NEWS STORY
Published on 26 Mar 2026
Brain-inspired AI hardware helps autonomous devices operate efficiently and independently
A Purdue University engineer uses the brain’s efficiency as inspiration to help autonomous vehicles, such as drones and robots, make crucial, time-sensitive decisions while operating in the field.