Bharat Kaul | DeepAI

DeepAI

Featured Co-authors

Balaraman Ravindran
70 publications
Dipankar Das
35 publications
Dheevatsa Mudigere
23 publications
Anand Raghunathan
22 publications
Sasikanth Avancha
18 publications
Jongsoo Park
18 publications
Pradeep Dubey
17 publications
Theodore L. Willke
16 publications
Alexander Heinecke
16 publications
Jiyan Yang
16 publications
Kunal Banerjee
12 publications

∙ 04/16/2021

Efficient and Generic 1D Dilated Convolution Layer for Deep Learning

Convolutional neural networks (CNNs) have found many applications in tas...

Narendra Chaudhary, et al. ∙

∙ 04/12/2021

AI Powered Compiler Techniques for DL Code Optimization

Creating high performance implementations of deep learning primitives on...

Sanket Tavarageri, et al. ∙

∙ 03/19/2021

GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks

Graph Neural Networks (GNNs) use a fully-connected layer to extract feat...

Jacob R. Stevens, et al. ∙

∙ 06/02/2020

PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives

Deep Neural Networks (DNNs) have revolutionized many aspects of our live...

Sanket Tavarageri, et al. ∙

∙ 02/06/2020

PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives

At the heart of deep learning training and inferencing are computational...

Sanket Tavarageri, et al. ∙

∙ 01/15/2020

SEERL: Sample Efficient Ensemble Reinforcement Learning

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

Rohan Saphal, et al. ∙

∙ 09/17/2019

K-TanH: Hardware Efficient Activations For Deep Learning

We propose K-TanH, a novel, highly accurate, hardware efficient approxim...

Abhisek Kundu, et al. ∙

∙ 08/29/2019

High Performance Scalable FPGA Accelerator for Deep Neural Networks

Low-precision is the first order knob for achieving higher Artificial In...

Sudarshan Srinivasan, et al. ∙

∙ 06/11/2019

Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support

The deep neural networks (DNNs) have been enormously successful in tasks...

Sanket Tavarageri, et al. ∙

∙ 05/29/2019

Mixed Precision Training With 8-bit Floating Point

Reduced precision computation for deep neural networks is one of the key...

Naveen Mellempudi, et al. ∙

∙ 05/29/2019

A Study of BFLOAT16 for Deep Learning Training

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

Dhiraj Kalamkar, et al. ∙

∙ 09/04/2018

Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers

As deep learning methods form a critical part in commercially important ...

Apoorv Vyas, 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...

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...

Srinivas Sridharan, et al. ∙

∙ 07/20/2017

RAIL: Risk-Averse Imitation Learning

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

Anirban Santara, et al. ∙

∙ 07/15/2017

Ternary Residual Networks

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

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...

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...

Naveen Mellempudi, et al. ∙