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Motomura-Fujiki Lab
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The lab develops new hardware that carries out AI computation efficiently.
AboutAbout the lab
Creating a new AI platform.
The Motomura-Fujiki Lab researches information processing architectures and hardware for AI workloads.
The two labs work together as the AI Computing Research Unit (ArtIC), launched in April 2019.
The labs approach problems from both the software and hardware sides, and value coming up with ideas different from others, not just improving performance numbers.
The Motomura Lab is not accepting new students; students who wish to join ArtIC are advised to apply to the Fujiki Lab.
Key terms
- Architecture
- The basic design of a computer. It determines how the parts that compute and the parts that store data are combined.
- Accelerator
- A dedicated circuit built to perform only a specific kind of computation quickly and with low power.
- Combinatorial optimization problem
- A problem of finding the best option among a huge number of possible combinations of choices.
- FPGA
- A semiconductor chip whose internal circuit configuration can be rewritten even after it has been manufactured.
NewsLab news
Paper accepted at the international conference ICIP 2026
Paper accepted at IEEE Trans. Evol. Comput.
Paper accepted at the international conference WACV 2026
Paper accepted at the international conference ICLR 2026
Paper accepted at the journal IEEE Access
ResearchResearch themes
The lab aims to realize AI computing, including not only deep neural networks but also conventional machine learning and big data processing, on hardware with high energy efficiency and cost efficiency. Building on the idea of “reconfigurable hardware,” whose circuit configuration can be switched to match the processing task, the lab is developing the foundation for its research and development.
*Illustrative imageAnnealing Processors
To solve large, complex combinatorial optimization problems quickly, the lab devised a new annealing method that can update all spins in parallel, and developed a processor LSI that uses it, “STATICA.”
*Illustrative imageDedicated Circuits for Neural Networks
The lab researches accelerators that perform neural network computation efficiently. Its work includes architectures that use quantization algorithms to reduce computation and memory.
*Illustrative imageSpeeding Up Ensemble Learning
The lab researches hardware that makes the training of “ensemble learning,” which combines multiple models, faster and more power-efficient. This has included an FPGA-based training accelerator for random forests.
*Illustrative imageImage-Based Object Recognition
For driver assistance systems and autonomous mobile robots, the lab researches object recognition, from algorithms to hardware, that achieves both high recognition accuracy and real-time processing at low power.
MembersFaculty
The Motomura-Fujiki Lab's members include faculty, secretaries, postdoctoral researchers, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorMasato MotomuraPublications ↗
- Associate ProfessorDaichi FujikiPublications ↗
- Assistant ProfessorTatsuya KanekoPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Freescale MPC8245 integrated processor die shot” Martijn Boer / Public domain (cropped)
- Annealing Processors: “Ising Criticality2” Damian Owls / CC BY-SA 4.0 (cropped)
- Dedicated Circuits for Neural Networks: “STM32F103VGT6-SRAM” ZeptoBars / CC BY 3.0 (cropped)
- Speeding Up Ensemble Learning: “Decision Tree vs. Random Forest” Jeremybeauchamp / CC BY-SA 4.0 (cropped)
- Image-Based Object Recognition: “MPC-inside-6x4 ff” Dr.-Ing. Juergen Seekircher / CC BY-SA 3.0 (cropped)
- Related lab (佐々
木研究室): “Intel-pentium-ii-dixon-die-shot-high-resolution-stitched” Martijn Boer / Public domain (cropped) - Related lab (Thiem研究室): “Circuit Board closeup ( )” Dennis van Zuijlekom / CC BY-SA 2.0 (cropped)
- Related lab (一色研究室): “Circuit image on Rubylith” Camera Operator: TSGT Bob Wickley / Public domain (cropped)
- Related lab (渡辺研究室): “Mapping a l'Hospital de Sant Pau - 1 de gener” Jordiferrer / CC BY-SA 4.0 (cropped)
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