no code implementations • 18 Apr 2024 • Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers.
no code implementations • 17 Apr 2024 • Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Avisek Naug, Alexandre Pichard, Mathieu Cocho
Our results show that the transformer model of moderate depth with gated residual connections around the multi-head attention, multi-layer perceptron, and the transformer block (STrXL) proposed in this paper is optimal and boosts energy efficiency by an average of 22. 1% for these complex spread waves over the existing spring damper (SD) controller.
1 code implementation • 16 Apr 2024 • Soumyendu Sarkar, Avisek Naug, Antonio Guillen, Ricardo Luna, Vineet Gundecha, Ashwin Ramesh Babu, Sajad Mousavi
To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs.
no code implementations • 27 Mar 2024 • Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Avisek Naug, Sahand Ghorbanpour
We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D).
no code implementations • 21 Mar 2024 • Soumyendu Sarkar, Avisek Naug, Ricardo Luna, Antonio Guillen, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Dejan Markovikj, Ashwin Ramesh Babu
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide.
no code implementations • 28 Oct 2023 • Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan, Vineet Gundecha, Ashwin Ramesh Babu, Avisek Naug, Antonio Guillen, Soumyendu Sarkar
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination.
no code implementations • 28 Oct 2023 • Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Zachariah Carmichael, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna, Gutierrez Antonio Guillen, Avisek Naug
We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models.
no code implementations • 5 Oct 2023 • Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Antonio Guillen, Soumyendu Sarkar
In this paper, we combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning.
no code implementations • 5 Oct 2023 • Avisek Naug, Antonio Guillen, Ricardo Luna Gutiérrez, Vineet Gundecha, Dejan Markovikj, Lekhapriya Dheeraj Kashyap, Lorenz Krause, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar
The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation.
no code implementations • 13 Sep 2022 • Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Alexandre Pichard, Mathieu Cocho
Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 14 Oct 2019 • Pei Yang, Srinivas Varadharajan, Lucas A. Wilson, Don D. Smith II, John A. Lockman III, Vineet Gundecha, Quy Ta
Restricted Boltzmann Machine (RBM) is a generative stochastic neural network that can be applied to collaborative filtering technique used by recommendation systems.