Dheevatsa Mudigere | DeepAI

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Alejandro Ribeiro
142 publications
James Zou
123 publications
Kurt Keutzer
112 publications
Ying Zhang
107 publications
Lin Yang
100 publications
Meng Li
82 publications
Balaraman Ravindran
75 publications
Xiaodong Wang
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Trung Le
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∙ 09/12/2023

A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Shampoo is an online and stochastic optimization algorithm belonging to ...

0 Hao-Jun Michael Shi, et al. ∙

∙ 04/19/2023

MTrainS: Improving DLRM training efficiency using heterogeneous memories

Recommendation models are very large, requiring terabytes (TB) of memory...

0 Hiwot Tadese Kassa, et al. ∙

∙ 03/28/2022

Learning to Collide: Recommendation System Model Compression with Learned Hash Functions

A key characteristic of deep recommendation models is the immense memory...

0 Benjamin Ghaemmaghami, et al. ∙

∙ 02/01/2022

TopoOpt: Optimizing the Network Topology for Distributed DNN Training

We explore a novel approach for building DNN training clusters using com...

0 Weiyang Wang, et al. ∙

∙ 10/25/2021

Differentiable NAS Framework and Application to Ads CTR Prediction

Neural architecture search (NAS) methods aim to automatically find the o...

11 Ravi Krishna, et al. ∙

∙ 10/21/2021

Supporting Massive DLRM Inference Through Software Defined Memory

Deep Learning Recommendation Models (DLRM) are widespread, account for a...

0 Ehsan K. Ardestani, et al. ∙

∙ 04/12/2021

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many busines...

32 Dheevatsa Mudigere, et al. ∙

∙ 10/17/2020

Check-N-Run: A Checkpointing System for Training Recommendation Models

Checkpoints play an important role in training recommendation systems at...

0 Assaf Eisenman, et al. ∙

∙ 03/20/2020

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems

Large-scale training is important to ensure high performance and accurac...

0 Maxim Naumov, et al. ∙

∙ 01/15/2020

SEERL: Sample Efficient Ensemble Reinforcement Learning

Ensemble learning is a very prevalent method employed in machine learnin...

43 Rohan Saphal, et al. ∙

∙ 12/30/2019

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

Personalized recommendation systems leverage deep learning models and ac...

0 Liu Ke, et al. ∙

∙ 09/25/2019

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

In many real-world applications, e.g. recommendation systems, certain it...

0 Antonio Ginart, et al. ∙

∙ 09/04/2019

Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems

Modern deep learning-based recommendation systems exploit hundreds to th...

1 Hao-Jun Michael Shi, et al. ∙

∙ 06/06/2019

The Architectural Implications of Facebook's DNN-based Personalized Recommendation

The widespread application of deep learning has changed the landscape of...

0 Udit Gupta, et al. ∙

∙ 05/31/2019

Deep Learning Recommendation Model for Personalization and Recommendation Systems

With the advent of deep learning, neural network-based recommendation mo...

0 Maxim Naumov, et al. ∙

∙ 05/29/2019

A Study of BFLOAT16 for Deep Learning Training

This paper presents the first comprehensive empirical study demonstratin...

0 Dhiraj Kalamkar, et al. ∙

∙ 10/26/2018

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

In this paper, we propose a Distributed Accumulated Newton Conjugate gra...

0 Majid Jahani, et al. ∙

∙ 02/15/2018

A Progressive Batching L-BFGS Method for Machine Learning

The standard L-BFGS method relies on gradient approximations that are no...

0 Raghu Bollapragada, et al. ∙

∙ 02/03/2018

Mixed Precision Training of Convolutional Neural Networks using Integer Operations

The state-of-the-art (SOTA) for mixed precision training is dominated by...

0 Dipankar Das, et al. ∙

∙ 01/24/2018

On Scale-out Deep Learning Training for Cloud and HPC

The exponential growth in use of large deep neural networks has accelera...

0 Srinivas Sridharan, et al. ∙

∙ 07/20/2017

RAIL: Risk-Averse Imitation Learning

Imitation learning algorithms learn viable policies by imitating an expe...

0 Anirban Santara, et al. ∙

∙ 07/15/2017

Ternary Residual Networks

Sub-8-bit representation of DNNs incur some discernible loss of accuracy...

0 Abhisek Kundu, et al. ∙

∙ 05/02/2017

Ternary Neural Networks with Fine-Grained Quantization

We propose a novel fine-grained quantization (FGQ) method to ternarize p...

0 Naveen Mellempudi, et al. ∙

∙ 01/31/2017

Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point

We propose a cluster-based quantization method to convert pre-trained fu...

0 Naveen Mellempudi, et al. ∙

∙ 11/16/2010

Fast GPGPU Data Rearrangement Kernels using CUDA

Many high performance-computing algorithms are bandwidth limited, hence ...

0 Michael Bader, et al. ∙

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