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Found 7 papers, 2 papers with code
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.
Iterative Oversampling Technique for Constraint Energy Minimizing Generalized Multiscale Finite Element Method in the Mixed Formulation
The iterative procedure starts with the construction of an energy minimizing snapshot space that can be used for approximating the solution of the model problem.
Numerical Analysis Numerical Analysis
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point
In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware.
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem
In this work, we propose a multi-agent actor-critic reinforcement learning (RL) algorithm to accelerate the multi-level Monte Carlo Markov Chain (MCMC) sampling algorithms.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Computational multiscale methods for quasi-gas dynamic equations
This multiscale problem is interesting from a multiscale methodology point of view as the model problem has a hyperbolic multiscale term, and designing multiscale methods for hyperbolic equations is challenging.
Numerical Analysis Numerical Analysis 65M22, 65M60
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
