Zobrazeno 1 - 10
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pro vyhledávání: '"Deboleena Roy"'
Publikováno v:
Frontiers in Neuroscience, Vol 13 (2019)
Spiking neural networks (SNNs) offer a promising alternative to current artificial neural networks to enable low-power event-driven neuromorphic hardware. Spike-based neuromorphic applications require processing and extracting meaningful information
Externí odkaz:
https://doaj.org/article/384bccd10f07407389b78908073bdab6
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 30:1448-1460
Applications based on Deep Neural Networks (DNNs) have grown exponentially in the past decade. To match their increasing computational needs, several Non-Volatile Memory (NVM) crossbar based accelerators have been proposed. Recently, researchers have
Publikováno v:
IEEE Design & Test. 38:28-35
The advancements of the Internet of Things (IoT) and Artificial Intelligence (AI) have resulted in the proliferation of intelligent computing devices. However, AI-based solutions are often compute-intensive, severely limiting the performance of edge
Autor:
Kaushik Roy, Minsuk Koo, Deboleena Roy, Abhronil Sengupta, Saima Sharmin, Utkarsh Saxena, Amogh Agrawal, Chamika Liyanagedera, Indranil Chakraborty, Yong Shim, Gopalakrishnan Srinivasan
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:2481-2494
In this era of nanoscale technologies, the inherent characteristics of some nonvolatile devices, such as resistive random access memory (ReRAM), phase-change material (PCM), and spintronics, can emulate stochastic functionalities. Traditionally, thes
Autor:
Deboleena Roy
Publikováno v:
Catalyst: Feminism, Theory, Technoscience. 8
In this article, the feminist and postcolonial frameworks of distributed reproduction and frontstaging a chemical are used to further explore the concept of global fertility chains. In particular, these approaches are used to trace reproductive bioec
Publikováno v:
Neural Networks. 121:148-160
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to
Publikováno v:
DAC
The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ Matrix Vector
Dissertation/ Thesis
Autor:
Deboleena Roy (11181642)
In the past fifty years, Deep Neural Networks (DNNs) have evolved greatly from a single perceptron to complex multi-layered networks with non-linear activation functions. Today, they form the backbone of Artificial Intelligence, with a diverse applic
Autor:
Amogh Agrawal, Bing Han, Akhilesh Jaiswal, Aayush Ankit, Deboleena Roy, Kaushik Roy, Gopalakrishnan Srinivasan
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers. 66:3064-3076
Deep neural networks are biologically inspired class of algorithms that have recently demonstrated the state-of-the-art accuracy in large-scale classification and recognition tasks. Hardware acceleration of deep networks is of paramount importance to
Autor:
Banu Subramaniam, Deboleena Roy
Publikováno v:
Mattering ISBN: 9781479842308
Mattering: Feminism, Science, and Materialism
Mattering: Feminism, Science, and Materialism
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1e2b68007c3bbe6c2e1af01065d742e
https://doi.org/10.18574/nyu/9781479833498.003.0002
https://doi.org/10.18574/nyu/9781479833498.003.0002