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pro vyhledávání: '"Kim, Ryan Gary"'
To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed. However, designing such complex system
Externí odkaz:
http://arxiv.org/abs/2303.06169
Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching. Recently, reversible multi-function channel (RMC) buffers have been proposed to simultaneously reduce power and e
Externí odkaz:
http://arxiv.org/abs/2205.13130
Autor:
Arka, Aqeeb Iqbal, Joardar, Biresh Kumar, Kim, Ryan Gary, Kim, Dae Hyun, Doppa, Janardhan Rao, Pande, Partha Pratim
Heterogeneous manycore architectures are the key to efficiently execute compute- and data-intensive applications. Through silicon via (TSV)-based 3D manycore system is a promising solution in this direction as it enables integration of disparate comp
Externí odkaz:
http://arxiv.org/abs/2012.00102
Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory process
Externí odkaz:
http://arxiv.org/abs/2009.09603
Monolithic 3D (M3D) technology enables high density integration, performance, and energy-efficiency by sequentially stacking tiers on top of each other. M3D-based network-on-chip (NoC) architectures can exploit these benefits by adopting tier partiti
Externí odkaz:
http://arxiv.org/abs/1906.04293
Autor:
Joardar, Biresh Kumar, Kim, Ryan Gary, Doppa, Janardhan Rao, Pande, Partha Pratim, Marculescu, Diana, Marculescu, Radu
Publikováno v:
IEEE Transactions on Computers, vol. 68, no. 6, June 2019
The rising use of deep learning and other big-data algorithms has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. Due to the amount of data parallelism in these algorithms, high-performance
Externí odkaz:
http://arxiv.org/abs/1810.08869
Autor:
Choi, Wonje, Duraisamy, Karthi, Kim, Ryan Gary, Doppa, Janardhan Rao, Pande, Partha Pratim, Marculescu, Diana, Marculescu, Radu
Convolutional Neural Networks (CNNs) have shown a great deal of success in diverse application domains including computer vision, speech recognition, and natural language processing. However, as the size of datasets and the depth of neural network ar
Externí odkaz:
http://arxiv.org/abs/1712.02293
Autor:
Kim, Ryan Gary, Doppa, Janardhan Rao, Pande, Partha Pratim, Marculescu, Diana, Marculescu, Radu
Tight collaboration between experts of machine learning and manycore system design is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the ri
Externí odkaz:
http://arxiv.org/abs/1712.00076
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Publikováno v:
IEEE Transactions on Computers; August 2023, Vol. 72 Issue: 8 p2278-2292, 15p