【参加募集】MIT-Toyohashi ASPIRE workshop 2025

[Yukinori Sato]

PROの皆様

お世話になっております。豊橋技科大の佐藤です。

MIT-Toyohashi ASPIRE workshop 2025の参加募集をさせていただきます。
6月26日（木）に東京科学大学の蔵前会館 2F 大会議室にて開催します。
最新のコンパイラ技術に関する講演や、JST ASPIREプロジェクトにおけるAIアクセラレータやネットワークパケット解析向けのDSL開発に関する講演があります。
参加費は無料です。午前中のセッションのみ、Zoomでの配信を予定しております。
多くの皆様のご参加を心よりお待ちしております。

どうぞよろしくお願い致します．

佐藤幸紀
豊橋技術科学大学
大学院工学研究科 情報・知能工学系
Email:　yuki...@cs.tut.ac.jp
https://www.perf.cs.tut.ac.jp/~yukinori


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MIT-Toyohashi ASPIRE workshop 2025
https://www.perf.cs.tut.ac.jp/lab/?page_id=1078&lang=en

Date: 26 June, 2025
Location: Conference Room L, 2nd floor,
Science Tokyo Front (Kuramae-Kaikan、蔵前会館 2F 大会議室)
Ookayama Campus, Institute of Science Tokyo

Program (tentative)
**All programs are in JST (UTC+9)
10:00 JST ASPIRE project introduction (Yukinori Sato, Toyohashi Univ. of Tech.)
10:15 Saman Amarasinghe (MIT): Compiler 2.0: Building Compilers in
the Era of Machine Learning
11:15 Ajay Brahmakshatriya (MIT): NetBlocks [PLDI 2024] (tentative).
12:15 Lunch Break
12:45 Poster presentations by students
13:45 Hayato Yamaki (UEC): High-Speed Packet Compression and Forwarding System.
14:15 Yudai Tanabe (Science Tokyo): Bringing Fine-Grained Task
Parallelism to GPUs
14:45 Break
15:00 Katsumi Okuda (Mitsubishi Electric)
15:30 Ryuichi Sakamoto (Science Tokyo): Accelerating microservice
using SmartNICs
16:00 Yukinori Sato (Toyohashi Univ. of Tech.): DSL-based automatic
code optimization for AI accelerators and SmartNICs
17:00 close

We plan to prepare Zoom in the morning sessions (10:00am - 12:15pm)

Please make a registration from the following URL:
https://forms.gle/1co46Xc1iXaA2bHg9

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Compiler 2.0: Building Compilers in the Era of Machine Learning
Abstract: Modern compilers are among the most critical, complex, and
widely used software systems—yet they are still largely built on
decades-old technologies. The rise of machine learning is
fundamentally reshaping what programming means. It not only has the
potential to revolutionize how we build compilers but may even render
traditional compilers obsolete. In this talk, I will explore the
current impact of machine learning on compilers, discuss near-term
opportunities, and offer a few predictions about what the future may
hold.

Saman Amarasinghe is the Thomas and Gerd Perkins Professor in the
Department of Electrical Engineering and Computer Science at the
Massachusetts Institute of Technology and a member of the Computer
Science and Artificial Intelligence Laboratory (CSAIL), where he leads
the Commit compiler group. Under his leadership, the Commit group has
developed a wide range of innovative programming languages and
compilers, including StreamIt, StreamJIT, PetaBricks, Halide, TACO,
Finch, SySTeC, GraphIt, Simit, MILK, Cimple, BioStream, NetBlocks,
BREeze, CoLa, Shim, AskIt, and Seq. Additionally, the group has
created compiler and runtime frameworks such as DynamoRIO, Helium,
Tiramisu, Codon, BuildIt, and D2X as well as tools for vectorization
like Superword Level Parallelism (SLP), goSLP, and VeGen. Saman’s team
also developed Ithemal, a machine-learning-based performance
predictor, Program Shepherding to protect programs from external
attacks, the OpenTuner extendable autotuner, and the Kendo
deterministic execution system. He was also co-leader of the Raw
architecture project. Outside academia, Saman has co-founded several
companies, including Determina, Lanka Internet Services Ltd., Venti
Technologies, DataCebo, and Exaloop. He earned his BS in Electrical
Engineering and Computer Science from Cornell University in 1988, and
his MSEE and Ph.D. from Stanford University in 1990 and 1997,
respectively. He is also a Fellow of the ACM.