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1st Runner-Up and Kyushu Keisokki Co., Ltd. Award at the 29th LSI Design Contest

We are delighted to share that our team (M1 Hanyu, M1 Atharv, and B3 Komatsuzaki) received the 1st Runner-Up Prize (Kyushu Keisokki Co., Ltd. Award) at the 29th LSI Design Contest, held in Okinawa, Japan.

The LSI Design Contest is an international competition where student teams present innovative LSI, VLSI, and hardware system designs to a panel of experts from academia and industry. The contest provides an excellent platform for students to demonstrate practical chip and system design skills, as well as creative ideas in advanced computing architectures.

In 2026, the topic of the 29th edition of the competition is Generative Adversarial Network (GAN). Over 77 participants (26 teams) from Japan and overseas applied. After the screening round, 11 teams are invited (with financial support) to present at the venue in Okinawa on March 6, 2026.

Our team presented a project on Spiking Generative Adversarial Network (Spiking GAN). The work explores how generative models can be implemented using spiking neural networks, enabling energy-efficient generative AI suitable for neuromorphic and edge computing platforms.

The project demonstrates a GAN architecture adapted to spike-based neural computation, highlighting how spiking processing can be applied to generative tasks while maintaining the efficiency advantages of neuromorphic systems.

The project was recognized for its novelty, technical contribution, and presentation quality, earning both the Runner-Up Prize and the Kyushu Keisokki Co., Ltd. Award.

We are extremely proud of the students for their hard work, dedication, and outstanding performance, representing our research group and university at this international competition.

  • A moment from the LSI Design Contest presentation in Okinawa of Mr. Hanyu, Mr. Atharv with the award:

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Some extra links:

Congratulations to the entire team for this excellent achievement.
We look forward to more exciting innovations from our students in neuromorphic AI and hardware design.