Zobrazeno 1 - 10
of 254
pro vyhledávání: '"Das, Chita"'
Autor:
Jain, Rishabh, Bhasi, Vivek M., Jog, Adwait, Sivasubramaniam, Anand, Kandemir, Mahmut T., Das, Chita R.
Personalized recommendation is a ubiquitous application on the internet, with many industries and hyperscalers extensively leveraging Deep Learning Recommendation Models (DLRMs) for their personalization needs (like ad serving or movie suggestions).
Externí odkaz:
http://arxiv.org/abs/2410.22249
As continuous learning based video analytics continue to evolve, the role of efficient edge servers in efficiently managing vast and dynamic datasets is becoming increasingly crucial. Unlike their compute architecture, storage and archival system for
Externí odkaz:
http://arxiv.org/abs/2410.05435
Autor:
Mishra, Cyan Subhra, Sampson, Jack, Kandmeir, Mahmut Taylan, Narayanan, Vijaykrishnan, Das, Chita R
There is an increasing demand for intelligent processing on ultra-low-power internet of things (IoT) device. Recent works have shown substantial efficiency boosts by executing inferences directly on the IoT device (node) rather than transmitting data
Externí odkaz:
http://arxiv.org/abs/2408.14379
The deployment of Deep Neural Networks in energy-constrained environments, such as Energy Harvesting Wireless Sensor Networks, presents unique challenges, primarily due to the intermittent nature of power availability. To address these challenges, th
Externí odkaz:
http://arxiv.org/abs/2408.13696
Autor:
Sharma, Aakash, Bhasi, Vivek M., Singh, Sonali, Kesidis, George, Kandemir, Mahmut T., Das, Chita R.
We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the anticipated communication-network delays. Our scheduler consists of th
Externí odkaz:
http://arxiv.org/abs/2401.16492
Autor:
Sharma, Aakash, Bhasi, Vivek M., Singh, Sonali, Jain, Rishabh, Gunasekaran, Jashwant Raj, Mitra, Subrata, Kandemir, Mahmut Taylan, Kesidis, George, Das, Chita R.
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 while running on a public cloud. We have implemented the profiler by exte
Externí odkaz:
http://arxiv.org/abs/2208.14344
Autor:
Sarma, Anup, Singh, Sonali, Jiang, Huaipan, Pattnaik, Ashutosh, Mishra, Asit K, Narayanan, Vijaykrishnan, Kandemir, Mahmut T, Das, Chita R
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 image classification and object detection. However, training these mode
Externí odkaz:
http://arxiv.org/abs/2109.07710
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 tasks in text and speech. Training of such LSTM applications is compu
Externí odkaz:
http://arxiv.org/abs/2106.12089
Autor:
Gunasekaran, Jashwant Raj, Mishra, Cyan Subhra, Thinakaran, Prashanth, Kandemir, Mahmut Taylan, Das, Chita R.
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 predic-tions with minimal latency along with reduced deploymentcosts in a
Externí odkaz:
http://arxiv.org/abs/2106.05345
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 scale data processing frameworks such as MapReduce, Tez, Spark etc. to pr
Externí odkaz:
http://arxiv.org/abs/2009.04561