Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Sina Sayyah Ensan"'
Publikováno v:
Frontiers in Nanotechnology, Vol 3 (2022)
Spiking Neural Networks (SNN) are fast emerging as an alternative option to Deep Neural Networks (DNN). They are computationally more powerful and provide higher energy-efficiency than DNNs. While exciting at first glance, SNNs contain security-sensi
Externí odkaz:
https://doaj.org/article/59294635a7ba42048bf85ba3b02083de
Autor:
Sina Sayyah Ensan, Swaroop Ghosh
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29:237-241
Data movement between memory and processing units poses an energy barrier to Von-Neumann-based architectures. In-memory computing (IMC) eliminates this barrier. RRAM-based IMC has been explored for data-intensive applications, such as artificial neur
Autor:
Ali Afzali-Kusha, Shaahin Hessabi, Sina Sayyah Ensan, Mohammad Hossein Moaiyeri, Behzad Ebrahimi
Publikováno v:
Journal of Computational Electronics. 18:519-526
This paper presents a novel low-leakage and high-writable 8T SRAM cell based on FinFET technology. This cell reduces leakage current and consequently leakage power by dynamically adjusting the back gate of the stacked independent-gate FinFET devices.
Publikováno v:
AEU - International Journal of Electronics and Communications. 99:361-368
This paper presents a single-ended low-power 7T SRAM cell in FinFET technology. This cell enhances read performance by isolating the storage node from the read path. Moreover, disconnecting the feedback path of the cross-coupled inverters during the
Autor:
Swaroop Ghosh, Asmit De, Mohammad Nasim Imtiaz Khan, Abdullah Ash-Saki, Sina Sayyah Ensan, Karthikeyan Nagarajan
Publikováno v:
MWSCAS
Emerging Non-Volatile Memories (NVM) have shown enormous potential for wide applications within the memory hierarchy to replace and/or assist conventional CMOS technology. In addition to key advantages of having low leakage power and low footprint, t
In-memory computing architectures provide a much needed solution to energy-efficiency barriers posed by Von-Neumann computing due to the movement of data between the processor and the memory. Functions implemented in such in-memory architectures are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99a0bef4a352080e0f72b0091c462088
Publikováno v:
Analog Integrated Circuits and Signal Processing. 94:497-506
This paper presents a robust and low-power single-ended robust 11T near-threshold SRAM cell in 10-nm FinFET technology. The proposed cell eliminates write disturbance and enhances write performance by disconnecting the path between cross-coupled inve
Autor:
Mohammad Nasim Imtiaz Khan, Karthikeyan Nagarajan, Swaroop Ghosh, Anupam Chattopadhyay, Sina Sayyah Ensan
Publikováno v:
ISLPED
In memory-computing (IMC) architectures provide a much needed solution to energy-efficiency barriers posed by Von-Neumann computing due to movement of data between the processor and the memory. Emerging non-volatile memories (NVM) such as Resistive R
Autor:
Swagata Mandal, Sina Sayyah Ensan, Karthikeyan Nagarajan, Swaroop Ghosh, Anupam Chattopadhyay
Publikováno v:
ISVLSI
Asymmetric code-based crypto-systems have been developed in the last decade due to rapid evolution of quantum computing that can potentially compromise RSA and ECC based crypto-systems. The McEliece crypto-system based on the general decoding problem
Autor:
Swaroop Ghosh, Sina Sayyah Ensan
Publikováno v:
IJCNN
Autonomous systems e.g., cars and drones generate vast amount of data from sensors that need to be processed in timely fashion to make accurate and safe decisions. Majority of these computations deal with Floating Point (FP) numbers. Conventional Von