16:35 – 16:50
An AI-Semiconductor Nexus: Custom 3D-Stacked Memory & Accelerated Materials Discovery
For the past decade, Preferred Networks has been at the forefront of co-creating physical AI solutions with industry-leading companies in Japan, while designing a novel AI ASIC series focused on energy-efficient compute to power the wider, unprecedented growth of AI-based applications. In this talk, we will explore An AI-Semiconductor Nexus—a synergistic acceleration driven by specialized generative AI ASICs and AI-based material discovery—as a key driving force for future industrial scalability. We will first examine how Custom 3D-Stacked DRAM Architectures can deliver the desperately needed memory bandwidth to sustain the massive throughput required by the shift toward longer context in AI agent applications. We will then look at the transformative potential of Accelerated Materials Discovery, where AI-driven workflows are drastically compressing the R&D timeline. This ensures that innovations in the physical hardware foundation can keep pace with the continued exponential growth of AI and its industrial applications.
Thank you again for all your support and arrangements, and sorry for all your trouble.Very much looking forward to meeting everyone at the event tomorrow.
Karim Hamzaoui
Preferred Networks
Karim Hamzaoui is the Vice President of Global Business Development at Preferred Networks, a leading AI unicorn in Japan advancing technologies from custom AI ASICs to fully integrated industrial solutions.
Karim began his career at IBM Japan’s Software R&D Laboratory as an engineer and later strategist, where he developed a strong passion for creating business value through deep-tech innovation. Since joining Preferred Networks, Karim has led a broad portfolio of industrial AI initiatives—from early-stage R&D to commercialization—across domains such as cancer diagnostics, autonomous driving, plant automation, materials discovery, and generative AI foundation models. He now oversees the company’s global go-to-market strategy and partnerships.
Outside his professional roles, Karim is a founding and current board member of the Moroccan Business Club in Japan (MBC-J), an NPO dedicated to strengthening economic and cultural ties between Morocco and Japan.
Preferred Networks
Company Profile
Preferred Networks (PFN) is a leading Japanese AI technology company known for its vertically integrated development of cutting-edge technologies—from custom AI chips, generative AI foundation models to real-world AI solutions and products. PFN plays a key role in advancing Japan’s AI sovereignty and has earned a strong domestic reputation. PFN’s AI solutions span across diverse industries including manufacturing, materials, energy, pharmaceuticals, healthcare, finance, retail, and entertainment. The company is currently developing a novel AI inference chip, leveraging its highly energy-efficient MN-Core™ logic and a new memory technology, with a release targeted for early 2027.
Company Products & Services
Preferred Networks (PFN) develops cutting-edge technologies across the AI value chain from semiconductors, supercomputers, generative AI foundation models to AI solutions and products:
MN-Core™ — a series of highly energy-efficient AI chips, which made a debut topping the Green500 list of the world’s most energy-efficient supercomputers three times in 2020/21. PFN is currently developing a new line of MN-Core chips focused on AI inference, set for release in early 2027.
Preferred Computing Platform™ (PFCP™︎) — a cloud-based service that provides users with access to the computing power of MN-Core 2. PFN has also announced a joint venture with Mitsubishi Corporation and IIJ aiming to launch an MN-Core™-based IaaS commercial service in 2026.
PLaMo™ — a series of Large Language Models developed fully from scratch by PFN, delivering top-tier performance in Japanese-language capabilities. These models power a wide range of AI applications in sectors such as finance, healthcare, and the public sector.
Matlantis™ — a cloud-based, high-speed universal atomistic simulator. Powered by a machine learning interatomic potential (MLIP), Matlantis significantly accelerates the simulation workflow in new materials discovery across industries such as semiconductor, automotive, and energy, by bypassing traditional heavy electronic state calculations.
