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Symposium

10月Symposium『 AI時代のデータエコシステム:ストレージから量子・HPCまで』

日 時
2026年10月16日(金) 10:00 〜 17:00 (終了後、懇親会)
場 所
Shimadzu Tokyo Innovation Plaza (神奈川県川崎市)

※同時通訳はございません

<英>  : 講演は英語で行われます。
<日>  : 講演は日本語で行われます。

リモート  : リモートで講演していただきます。会場には、いらっしゃいません。
オンサイト : 会場で講演していただきます。

2026年10月16日(金)

10:00-10:10

開催挨拶

IDEMA会長
高野 公史 氏

10:10-10:50
<リモート>

Hard Disk Drive Demand Growth and the AI Data Center Buildout

International Data Corporation(IDC)
Research Director,
Edward Burns 氏

HDD demand continues to climb as the ongoing AI-driven data center buildout adds to already-robust demand from traditional enterprise and cloud infrastructure. Beyond new capacity additions, a steady cycle of installed-base drive replacement and rising data center density goals are pushing shipments higher, as operators pack more capacity into the same physical footprint. Enterprises and cloud service providers continue to favor HDDs for several durable reasons. Cost per terabyte remains far lower than flash alternatives, making HDDs the economical choice for storing massive and growing data volumes. Performance, while not matching SSDs, is more than sufficient for the bulk of enterprise and cloud workloads, particularly mass-capacity and archival use cases. When factoring in acquisition cost, density, and operational efficiency, HDDs also deliver the lowest total cost of ownership at scale. Underpinning all of this is the relentless growth in data creation from AI training and inference to everyday enterprise operations which continues to expand the addressable base that HDDs are best positioned to serve.

10:50-11:30
<リモート>

From Magnetic Storage to Magnetic Computing: CRAM for the AI Era and Beyond

University of Minnesota and Besimax Inc
Jian-Ping Wang 氏

The HDD industry has spent decades advancing our ability to store, move, and reliably access enormous amounts of digital information. Artificial intelligence is now creating a new challenge: the energy and performance bottleneck is increasingly not the computation itself, but the movement of data between storage, memory, and processors. This emerging "memory wall" is becoming one of the fundamental limitations of AI systems. This talk will discuss how advances rooted in magnetic materials, spintronics, and MRAM can enable a new computing architecture called Computational Random Access Memory (CRAM). Rather than continuously moving data from memory to a separate processor, CRAM transforms memory itself into a programmable digital computing fabric, allowing logic and arithmetic operations to be performed directly where the data resides. CRAM retains random-access memory functionality while adding reconfigurable digital computation, creating a fundamentally different approach to reducing data movement. I will review the evolution from magnetic recording and MRAM toward compute-in-memory, the device and architectural principles behind CRAM, and the path from laboratory demonstrations to foundry-compatible silicon. Particular attention will be given to opportunities relevant to the HDD and storage industry, including intelligent HDD/SSD controllers, near-storage computing, data processing close to storage, and increasingly AI-driven storage systems. Finally, I will discuss how the same technology can scale from low-power edge and storage applications to large-scale AI inference, and why expertise developed by the HDD industry in magnetic materials, devices, reliability, signal processing, and high-volume manufacturing could play an important role in the next transition--from magnetic storage to magnetic computing.

11:30-12:10
<オンサイト>

HDD Technology for the AI Era: Scaling Capacity, Performance and the Storage Ecosystem

Western Digital Company
SVP & CTO
Carl Che 氏

The rapid growth of AI and cloud infrastructure is creating new requirements for storage capacity, performance, power efficiency, cost, and scalability. HDD technology will continue to evolve to address these changing needs through innovations across recording technology, media, heads, mechanics, electronics, interfaces, firmware, and system architecture. This presentation will discuss the long-term direction of HDD technology and how continued advances in areal density, drive architecture, and data throughput can enable substantially higher capacity and performance. We will also explore the growing importance of the broader storage ecosystem, including host interfaces, software, and system integration, as HDD capabilities advance beyond traditional architectures. Together, these developments can help HDD remain a foundational technology for cost-effective data storage in the AI era and beyond.

12:10-13:20

Lunch Time

①Shimadzu Tokyo Innovation Plaza Tour

②島津製作所の電子顕微鏡のご紹介を技術者の方にお話しを聞く!

13:20-14:00
<オンサイト>

Toward the Ultimate Potential of HDD-Based Data Storage

Seagate Technology
Sr Director, Seagate Research
Stephanie Hernandez 氏

This presentation highlights a recent Heat Assisted Magnetic Recording (HAMR) spin-stand demonstration of 8.1 TB/disk enabled by aggressive heads and media, shingled and two-dimensional magnetic recording, and multiple readback processing. Also, we discuss progress in ferroelectric data storage research supported by fundamental materials modeling. We conclude with examples of how artificial intelligence is being applied to accelerate model development, enhance simulation efficiency, and identify novel materials opportunities, helping guide the next generation of high-capacity storage technologies.

14:00-14:40
<オンサイト>

MSTC活動ご紹介

情報ストレージ研究推進機構(MSTC) 運営委員長
株式会社 東芝
総合研究所
先端デバイスR&Dセンター
シニアフェロー
山田 健一郎 氏

生成AIやクラウドサービスの普及が急拡大する中、これらを支えるデータセンターの需要が急速に高まっています。HDDを含めたデータストレージは、増え続けるデータを記録・保存する中核的な役割を担っており、さらなる深化が求められています。MSTC (Mass Storage Technology Consortium)は、データストレージ業界とアカデミアとをつなぐ、ワールドワイドな産学連携コンソーシアムであり、今回、MSTC活動のご紹介をいたします。併せて、東芝のHDD技術に関する取り組みについてもご紹介いたします。

14:40-15:20
<オンサイト>

「AI for Science」時代の次世代計算基盤:富岳NEXTの概要と階層化ストレージ戦略

理化学研究所 計算科学研究センター
運用技術部門
データ連携技術ユニット
ユニットリーダー
甲斐 俊彦 氏

「富岳NEXT」は、2030年頃の運用開始を目指し、AIとシミュレーションの融合(AI for Science)を核に設計されています。 ノード構成はCPUとGPUのヘテロ構成を採用し、ストレージは高速なSSDによる第1階層と、大容量の第2階層からなる2階層アーキテクチャを採用し、ゼッタスケール時代の膨大なデータ需要に対し、コストと容量の最適化を図る戦略についての現状を説明。

15:20-15:30

Break

15:30-16:10
<オンサイト>

AI native digital supply/network chain and the impacts of nano scale physics

東京大学
大学院 情報理工学系研究科
教授
江崎 浩 氏

The recent digital devices have entering into nano scale physics. Nano meter and nano second has changed the physical architecture and configuration for every single devices, including storage devices. For example, from parallel to serial or from scale out to scale up. This is because of very simple physical scale. As the history of IT industry's between centralized (Claimant-Server) and distributed (Peer-to-Peer) , the system architecture of AI system with Data Center has been entering into distributed system from centralized system. Especially with the introduction of Agentic AI and Physical AI, this system revolution has been accelerated. This is not only for data centers, but also for each company's' factories and laboratories.

16:10-16:50
<オンサイト>

IBMが考える量子コンピューターの未来

日本アイ・ビー・エム株式会社
三橋 朗 氏

量子コンピューターの現状と、今後どのように進化していくのかをIBMの量子コンピューターを例に説明します。また、どのようなユースケースがあり、またEcoシステムがどのようになっているかも説明致します。

16:50-17:00

閉会挨拶

17:10-

懇親会 (RIVER CAFE & DINING)

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