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Shruti R. Kulkarni
Researcher at Oak Ridge National Laboratory
Publications - 34
Citations - 407
Shruti R. Kulkarni is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Spiking neural network & Neuromorphic engineering. The author has an hindex of 4, co-authored 24 publications receiving 145 citations. Previous affiliations of Shruti R. Kulkarni include New Jersey Institute of Technology & Indian Institute of Technology Bombay.
Papers
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Journal ArticleDOI
Opportunities for neuromorphic computing algorithms and applications
Catherine D. Schuman,Shruti R. Kulkarni,Maryam Parsa,J. Parker Mitchell,Prasanna Date,Bill Kay +5 more
TL;DR: A review of recent results in neuromorphic computing algorithms and applications can be found in this article , where the authors highlight characteristics of neuromorphic Computing technologies that make them attractive for the future of computing and discuss opportunities for future development of algorithms and application on these systems.
Journal ArticleDOI
Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization
TL;DR: The proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision, shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications.
Journal ArticleDOI
Building Brain-Inspired Computing Systems: Examining the Role of Nanoscale Devices
TL;DR: Emulating the immense parallelism and event-driven computational architecture in systems with comparable complexity and power budget as the brain, and in real time, remains a formidable challenge.
Proceedings ArticleDOI
Neuromorphic Hardware Accelerator for SNN Inference based on STT-RAM Crossbar Arrays
TL;DR: The proposed STT-RAM based neurosynaptic core designed in 28 nm technology node has approximately 6× higher throughput per unit Watt and unit area than an equivalent SRAM based design and achieves ∼ 2× higher performance per Watt compared to other memristive neural network accelerator designs in the literature.
Proceedings ArticleDOI
Spiking neural networks — Algorithms, hardware implementations and applications
TL;DR: The learning algorithms, hardware demonstrations and potential applications of SNN based learning systems are reviewed.