About PULSE
PULSE is a complete AI-powered platform to create, train, manage and interact with predictive models for energy efficiency in data centers, particularly focused on PUE (Power Usage Effectiveness).
By integrating model generation, simulation, live prediction, and intelligent query capabilities via a dashboard interface, PULSE helps organizations optimize energy usage and gain actionable insights in a structured and intuitive way.
What can PULSE do?
PULSE is an intelligent platform for generating customized deep learning models, simulating energy efficiency scenarios in data centers and obtaining analytical answers using natural language.
And open-source solution
PULSE is an open-source platform built with a modern and modular technology stack, including TensorFlow and Keras for deep learning, Python and FastAPI for efficient backend services, and React with CoreUI for a responsive, professional frontend experience.
Adaptative and easy of integration
PULSE is designed to be highly adaptable to varied datasets, making it suitable for seamless integration across a wide range of data center environments. Its data ingestion and preprocessing pipeline supports heterogeneous input structures, allowing operators to work with the parameters most relevant to their infrastructure.
Predictive intelligence & Real-Time Simulation
PULSE integrates real-time simulation and predictive analytics to empower data center operators with actionable insights. By adjusting key operational variables such as temperature and server load, users can instantly observe the impact on PUE through interactive visualizations and live predictions.
Dataset explorer and analyzer
Visualize, filter, and understand your data with interactive tables and statistical plots to support accurate PUE modeling.
The Dataset Explorer & Analyzer module in PULSE allows users to inspect uploaded datasets through interactive data tables and dynamic visualizations such as histograms and correlation plots. This component supports comprehensive data understanding by enabling users to identify variable distributions, detect anomalies, and explore feature relationships. It serves as a crucial first step before model generation, ensuring that the input data is clean, well-structured, and informative for accurate PUE prediction.
Real-time PUE optimization
Simulate, compare, and optimize energy efficiency strategies dynamically using real operational parameters.
The Real-time PUE Optimization module in PULSE enables users to explore the impact of changing environmental and infrastructure variables on energy efficiency through an interactive simulation interface. Users can create multiple what-if scenarios by adjusting inputs such as temperature, workload, or cooling setpoints and instantly observe their effect on the predicted PUE. Each scenario can be saved, compared, and analyzed against historical data, facilitating data-driven decision-making. This feature empowers data center managers to proactively test optimization strategies and implement changes that align with performance goals and sustainability targets.
Why choose PULSE
PULSE should be chosen as a solution in data centers because it offers an integrated, technically robust platform that combines deep learning-based PUE modeling, real-time scenario simulation, and semantic interaction via LLMs
PULSE empowers data center operators to model and predict energy efficiency with precision.
PULSE provides a user-friendly pipeline to generate deep learning models from operational data. This enables organizations to tailor energy efficiency predictions to their own infrastructure without requiring advanced expertise in machine learning.
It enables dynamic scenario simulation to support real-time decision-making and sustainability goals.
Through its interactive simulation engine, PULSE allows users to manipulate environmental and operational variables to replicate conditions, forecast outcomes, and identify optimal configurations—all within a single interface.
By integrating LLMs, PULSE translates complex datasets into actionable insights through natural language.
With integrated Large Language Model support, PULSE bridges the gap between technical data and human understanding. Users can pose strategic or exploratory questions in plain English and receive data-driven responses contextualized to their environment.
What are you waiting for?
PULSE offers data centers a unified, intelligent solution that combines predictive modeling, real-time simulation, and natural language analytics to drive energy efficiency, operational insight, and sustainable performance—making it a strategic asset for modern infrastructure management.
Overview
Discover PULSE through some videos and screenshots. Wouldn’t you try it in your data center?
Research
Our research, our innovation
PULSE: A modular framework for predictive energy efficiency in heterogeneous data centers
Journal. Software X
Published in SoftwareX, this work presents PULSE, a modular and user-friendly platform that integrates deep learning and a natural language assistant to optimize data center energy efficiency. It enables accessible, data-driven decision-making and adapts to diverse infrastructures.
Improving Energy Efficiency in a Data Center: PUE Analyzing and Tuning
Conference. IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
Published in CCGRID, this work introduces a sensor-driven, machine learning-based methodology to optimize data center operations. By identifying key variables, integrating real-time monitoring, and uncovering hidden patterns, it improves efficiency, reduces energy consumption, and optimizes PUE.
PULSE: AI for Data Center Efficiency
Granted as Intellectual Property Innovation
PULSE has obtained intellectual property recognition, highlighting its innovative approach to data center energy efficiency through AI-driven prediction and simulation.
Contact us
Contact us to discover how PULSE can be seamlessly integrated into your data center operations to enhance energy efficiency, optimize performance, and accelerate your path toward sustainability.