Zeus AI Lab is the research division of Zeus, Inc., exploring ways to measure the energy used by AI learning in joules — and reduce it. Our flagship program, SETSUDEN, investigates low-bit AI models, energy-frugal learning, continual and edge learning, and rigorous methods for measuring their real energy cost.
Measure learning in joules, not just FLOPs.One flagship program, supported by three research pillars. We ask not only “How fast?” but also “What quality can we reach, and at what energy cost?”
Following the electricity constraints Japan faced after 2011, measuring power use and cutting what was unnecessary became a widely practiced form of energy conservation. We bring that mindset into AI research. SETSUDEN explores how far the compute and energy required for learning can be reduced while preserving capability and quality.
We study AI models that use low-bit representations, with the goal of reducing the compute and energy required for learning. We aim to make continued adaptation practical across diverse environments, including CPUs and low-power devices.
We study reproducible ways to evaluate the energy efficiency of AI learning using hardware counters and external power meters. The goal is to compare systems not only by speed, but by the quality reached per joule consumed.
Research judgment, acceptance or rejection of hypotheses, and design responsibility remain with humans. Multiple AI systems support review, verification, and the search for counter-evidence. We maintain preregistration and audit records to improve reproducibility.
We do not draw conclusions from estimates alone. We combine hardware counters and external power meters, and confirm results with physical measurement whenever practical.
Before an experiment, we define predictions and stopping criteria, then fix the conditions and analysis method before testing.
We look beyond the core learning loop and account for surrounding processing and evaluation when judging total energy use.
Mechanisms that fail — and the reasons why — are research assets that inform the next design.
After reviewing IP, confidentiality, and third-party licensing, we release the materials needed for reproducibility in stages.
SETSUDEN is currently undergoing validation and the necessary IP protection. We share the direction of the work while disclosing technical details and quantitative results in stages.
This page describes research directions. It does not guarantee commercial performance or general energy savings. Quantitative results will be released after validation and the necessary IP protection.
We value reproducible research while also respecting the intellectual property needed to translate research into practice. Unpublished algorithms, implementations, experimental conditions, and performance figures will be disclosed in stages after the necessary protection and validation.
A world in which AI can be trained only by those with massive clusters separates those who can participate from those who cannot. We aim for AI that can continue learning on CPUs and other accessible hardware. By reducing energy requirements, we hope to expand the places where learning AI can exist — including regions constrained by power infrastructure or capital. Zeus has provided free IT education in Myanmar since 2013, and that work comes from the same belief.
| Name | Zeus AI Lab — Research Division of Zeus, Inc. |
|---|---|
| Established | 2026 |
| Location | 4F-BC, 2-15-17 Nishi-Shimbashi, Minato-ku, Tokyo 105-0003, Japan |
| Lab Director | Masanori Katsura |
| Research Areas | Energy-frugal AI learning / Low-bit AI models / Continual learning & edge AI / Energy measurement methodology Flagship Program: SETSUDEN |
| Operated by | Zeus, Inc. (founded 2010 / AI, Web3 and systems development) |
For research collaboration, access to measurement environments, media inquiries, or career opportunities, please contact Zeus, Inc.
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