Members
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Team directorShinya Takamaeda
Introduction
As the demand for computational power and associated costs for deep learning continues to rise, energy-efficient and high-performance computing technologies are essential for further evolution and practical application of machine learning. Our team explores the design and realization method of advanced machine learning systems by working across multiple layers, including circuits, devices, architectures, and algorithms. Specifically, we investigate energy-efficient computer architecture for machine learning based on novel devices and computing principles, device-aware machine learning algorithms, and system architectures for large language model (LLM) inference serving that achieve low latency and high bandwidth with minimal energy consumption.
Algorithm/ Hardware Co-design
Domain-Specific Architecture
Compute in Memory
Computing with Emerging Devices
