Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Meenakshi Arunachalam"'
Publikováno v:
SIGMETRICS (Abstracts)
Deliberate use of approximate computing has been an active research area recently. Observing that many application programs from different domains can live with less-than-perfect accuracy, existing techniques try to trade off program output accuracy
Publikováno v:
Sustainable Computing: Informatics and Systems. 22:259-271
Hybrid multi-core processors are poised to dominate the landscape of next generation computing. By integrating several types of cores on a single chip, designers are anticipating sustained performance growth while depending less on raw circuit speed
Autor:
Jihyun Ryoo, Anup Sarma, Sharada Naveen, Mahmut Kandemir, Mengran Fan, Meenakshi Arunachalam, Huaipan Jiang
Publikováno v:
DATE
We propose a morphable convolution framework, which can be applied to irregularly shaped region of input feature map. This framework reduces the computational footprint of a regular CNN operation in the context of biomedical semantic image segmentati
Autor:
Meenakshi Arunachalam, Mengran Fan, Xulong Tang, Sharada Naveen, Jihyun Ryoo, Mahmut Kandemir, Huaipan Jiang
Publikováno v:
HiPC
The ever-growing complexity and popularity of machine learning and deep learning applications have motivated an urgent need of effective and efficient support for these applications on contemporary computing systems. In this paper, we thoroughly anal
Publikováno v:
PLDI
Minimizing cache misses has been the traditional goal in optimizing cache performance using compiler based techniques. However, continuously increasing dataset sizes combined with large numbers of cache banks and memory banks connected using on-chip
Publikováno v:
IGSC
CMT-bone is a proxy-app for simulating compressible multiphase turbulence. The application uses discretization and numerical methods for solving partial differential equations. Hence, the application is compute intensive as well as memory intensive.
Autor:
Mahmut Kandemir, Jagadish B. Kotra, Chita R. Das, Jihyun Ryoo, Anup Sarma, Huaipan Jiang, Meenakshi Arunachalam
Publikováno v:
SoCC
In recent years, deep convolutional networks have been widely used for a variety of visual recognition tasks, including biomedical applications. In most studies related to biomedical domain (e.g., cell tracking), the first step is to perform symmetri
Publikováno v:
ISQED
Scores of emerging and domain-specific applications need the ability to acquire and augment new knowledge from offline training-sets and online user interactions. This requires an underlying computing platform that can host machine learning (ML) kern
Autor:
Meenakshi, Arunachalam R.1, Kaliraja, Muniasamy1
Publikováno v:
Journal of Mathematical & Fundamental Sciences. 2013, Vol. 45A Issue 1, p83-92. 10p.
Publikováno v:
HPCC/CSS/ICESS
As an innovative design for high performance computing, Intel Xeon Phi coprocessor based on Intel Many Integrated Core (Intel MIC) architecture relies heavily on its SIMD (single instruction multiple data) unit. However, performance of non-contiguous