Zobrazeno 1 - 10
of 567
pro vyhledávání: '"SARASWAT, VIVEK A"'
Publikováno v:
Published 6 September 2023 IOP Publishing Ltd
Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory computing. Specifically, the Resistive Processing Unit (RPU) architecture
Externí odkaz:
http://arxiv.org/abs/2307.06088
Autor:
Patil, Shubham, Sakhuja, Jayatika, Singh, Ajay Kumar, Biswas, Anmol, Saraswat, Vivek, Kumar, Sandeep, Lashkare, Sandip, Ganguly, Udayan
Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-t
Externí odkaz:
http://arxiv.org/abs/2304.08504
Autor:
Meihar, Paritosh, Srinu, Rowtu, Saraswat, Vivek, Lashkare, Sandip, Mulaosmanovic, Halid, Singh, Ajay Kumar, Dünkel, Stefan, Beyer, Sven, Ganguly, Udayan
HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, high scalability, and CMOS compatibility. In this work, we show an n-cha
Externí odkaz:
http://arxiv.org/abs/2304.03124
Spiking Neural Networks (SNNs) have emerged as a hardware efficient architecture for classification tasks. The challenge of spike-based encoding has been the lack of a universal training mechanism performed entirely using spikes. There have been seve
Externí odkaz:
http://arxiv.org/abs/2207.09755
Autor:
Yoon, Hyojin, Truttmann, Tristan K., Liu, Fengdeng, Matthews, Bethany E., Choo, Sooho, Su, Qun, Saraswat, Vivek, Manzo, Sebastian, Arnold, Michael S., Bowden, Mark E., Kawasaki, Jason K., Koester, Steven J., Spurgeon, Steven R., Chambers, Scott A., Jalan, Bharat
The epitaxial growth of functional materials using a substrate with a graphene layer is a highly desirable method for improving structural quality and obtaining free-standing epitaxial nano-membranes for scientific study, applications, and economical
Externí odkaz:
http://arxiv.org/abs/2206.09094
Autor:
Saadh, Mohamed J., Kaur, Mandeep, Altalbawy, Farag M.A., Kaur, Harpreet, Saraswat, Vivek, Ibrahim, Abdullah Khaleel, Shuhata Alubiady, Mahmood Hasen, Zain Al-Abdeen, Salah Hassan, Shakier, Hussein Ghafel, Lawas, Amran Mezher, Ahmad, Irfan, Alhadrawi, Merwa
Publikováno v:
In Inorganic Chemistry Communications February 2025 172
Autor:
Saraswat, Vivek, Ganguly, Udayan
Emerging non-volatile memories have been proposed for a wide range of applications from easing the von-Neumann bottleneck to neuromorphic applications. Specifically, scalable RRAMs based on Pr$_{1-x}$Ca$_x$MnO$_3$ (PCMO) exhibit analog switching have
Externí odkaz:
http://arxiv.org/abs/2111.02885
Autor:
Lim, Zheng Hui, Manzo, Sebastian, Strohbeen, Patrick J., Saraswat, Vivek, Arnold, Michael S., Kawasaki, Jason K.
We demonstrate selective area epitaxy of GaAs films using patterned graphene masks on a Ge (001) substrate. The GaAs selectively grows on exposed regions of the Ge substrate, for graphene spacings as large as 10 microns. The selectivity is highly dep
Externí odkaz:
http://arxiv.org/abs/2111.01346
Autor:
Bhat, Muzzaffar A., Alabada, Rusul, Ajaj, Yathrib, Kaur, Mandeep, Kaur, Harpreet, Abduldayeva, Aigul, Sinha, Aashna, Saraswat, Vivek, Sood, Gaurav, Almarhoon, Zainab M., Butcher, Raymond J.
Publikováno v:
In Journal of Molecular Structure 5 August 2024 1309
Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computational efficiency by mimicking the human brain. It is desired to incorpor
Externí odkaz:
http://arxiv.org/abs/2108.13389