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|Title:||The N3XT approach to energy-efficient abundant-data computing||Authors:||Mohamed M. Sabry Aly
Wu, Tony F.
Malviya, Yash H.
Shulaker, Max M.
Wong, Philip H.-S.
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Mohamed M. Sabry Aly, Wu, T. F., Bartolo, A., Malviya, Y. H., Hwang, W., Hills, G., ... Mitra, S. (2019). The N3XT approach to energy-efficient abundant-data computing. Proceedings of the IEEE, 107(1), 19-48. doi:10.1109/jproc.2018.2882603||Journal:||Proceedings of the IEEE||Abstract:||The world's appetite for analyzing massive amounts of structured and unstructured data has grown dramatically. The computational demands of these abundant-data applications, such as deep learning, far exceed the capabilities of today's computing systems and are unlikely to be met with isolated improvements in transistor or memory technologies, or integrated circuit architectures alone. To achieve unprecedented functionality, speed, and energy efficiency, one must create transformative nanosystems whose architectures are based on the salient properties of the underlying nanotechnologies. Our Nano-Engineered Computing Systems Technology (N3XT) approach makes such nanosystems possible through new computing system architectures leveraging emerging device (logic and memory) nanotechnologies and their dense 3-D integration with fine-grained connectivity to immerse computing in memory and new logic devices (such as carbon nanotube field-effect transistors for implementing high-speed and low-energy logic circuits) as well as high-density nonvolatile memory (such as resistive memory), and amenable to ultradense (monolithic) 3-D integration of thin layers of logic and memory devices that are fabricated at low temperature. In addition, we explore the use of several device and integration technologies in the N3XT beyond the specific ones mentioned earlier that are also used in our main nanosystem prototypes. We also present an efficient resiliency technique to overcome endurance challenges in certain resistive memory technologies. N3XT hardware prototypes demonstrate the practicality of our architectures. We evaluate the benefits of the N3XT using a simulation framework calibrated using experimental measurements. System-level energy-delay product of common implementations of abundant-data workloads improves by three orders of magnitude in the N3XT compared with conventional architectures. These improvements impact a broad range of application workloads and architecture configurations, from embedded systems to the cloud.||URI:||https://hdl.handle.net/10356/143253||ISSN:||0018-9219||DOI:||10.1109/JPROC.2018.2882603||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JPROC.2018.2882603.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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