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Takamaeda Lab
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The lab designs highly efficient and trustworthy next-generation computers.
AboutAbout the lab
Highly efficient, trustworthy next-generation computing.
The Takamaeda Lab (CASYS) studies computer architecture, the field that deals with the structure and design techniques of computers.
Its work includes application-specific hardware, in-memory computing, methods for co-designing machine learning and hardware, and compilers that support hardware design.
The lab also researches software that makes computers easier to use, such as compilers and programming systems.
Because of building renovation, the faculty offices and the lab have temporarily relocated (as of September 2026).
Key terms
- Computer architecture
- The field that studies how processors and memory are combined and how a computer should be structured
- FPGA
- A semiconductor chip whose internal circuit configuration can be rewritten after purchase, making it easy to try out dedicated circuits
- High-level synthesis
- A technology that automatically generates hardware circuits from programs written in languages such as C or Python
NewsLab news
A student received the Outstanding Young Presenter Award from the IEICE Technical Committee on Computer Systems
A paper by a master's student was accepted to the international conference HEART 2026
A paper by a master's student was accepted to the international conference ISCA 2026
A master's student received the Young Researcher Encouragement Award from the IPSJ SIG on System and Architecture
A master's student received the Outstanding Young Presenter Award from the IEICE Technical Committee on Computer Systems
ResearchResearch themes
Centered on computer architecture, the lab studies highly efficient and trustworthy next-generation computing technologies. It develops dedicated hardware for neural networks (AI chips) and high-level synthesis tools that create FPGA circuits from programs.
*Illustrative imageAI Chips for Neural Networks
The lab studies the architecture of dedicated accelerators (AI chips) that run neural network inference quickly and with low power consumption.
*Illustrative imageLightweight, Highly Accurate Binarized Neural Networks
Binarized neural networks, which represent weights and values using only +1 and −1, have small circuits but suffer from low recognition accuracy. The lab developed a model that makes use of information previously discarded in the arithmetic circuits, improving accuracy while keeping the circuit small.
*Illustrative imageVeriloggen: Designing Circuits in Python
The lab has developed and released Veriloggen, a framework for designing hardware in Python. It covers everything from fine-grained circuit design to high-level synthesis written in terms of procedures and data flow, enabling efficient design of FPGA circuits.
*Illustrative imageNNgen: Generating Circuits from AI Models
The lab has developed and released NNgen, a compiler that automatically generates a dedicated accelerator circuit from a trained neural network model. The result can be tuned to the desired scale and performance without designing FPGA circuits by hand.
MembersFaculty
The members of the Takamaeda Lab consist of faculty, staff, and doctoral and master's students.As of 2026
- ProfessorShinya TakamaedaPublications ↗
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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: “5C2A5953R - – Silicon Wafer 20200519” Rob Bulmahn / CC BY 2.0 (cropped)
- AI Chips for Neural Networks: “ATI RV350 die shot” Martijn Boer / Public domain (cropped)
- Lightweight, Highly Accurate Binarized Neural Networks: “Freescale MPC8245 integrated processor die shot” Martijn Boer / Public domain (cropped)
- Veriloggen: Designing Circuits in Python: “Labyrinthine circuit board lines - Flickr - quapan” quapan / CC BY 2.0 (cropped)
- NNgen: Generating Circuits from AI Models: “Green circuit board II ( )” Peter Shanks from Hobart, Australia / CC BY 2.0 (cropped)
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