6 citations · 19 across the 10 of their papers we have counts for
11 papers
Analysis of Distributed Deep Learning in the Cloud
Aakash Sharma, Vivek M. Bhasi, Sonali Singh +6
We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from whi…
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs
Anup Sarma, Sonali Singh, Huaipan Jiang +5
Machine/deep-learning (ML/DL) based techniques are emerging as a driving force behind many cutting-edge technologies, achieving high accuracy on computer vision workloads such as i…
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Anup Sarma, Sonali Singh, Huaipan Jiang +3
Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning…
Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud
Jashwant Raj Gunasekaran, Cyan Subhra Mishra, Prashanth Thinakaran +2
With a growing demand for adopting ML models for a varietyof application services, it is vital that the frameworks servingthese models are capable of delivering highly accurate pre…
CASH: A Credit Aware Scheduling for Public Cloud Platforms
Aakash Sharma, Saravanan Dhakshinamurthy, George Kesidis +1
The public cloud offers a myriad of services which allows its tenants to process large scale big data in a flexible, easy and cost effective manner. Tenants generally use large sca…
Fifer: Tackling Underutilization in the Serverless Era
Jashwant Raj Gunasekaran, Prashanth Thinakaran, Nachiappan Chidambaram +2
Datacenters are witnessing a rapid surge in the adoption of serverless functions for microservices-based applications. A vast majority of these microservices typically span less th…