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Sina Sayyah Ensan

Researcher at Pennsylvania State University

Publications -  13
Citations -  142

Sina Sayyah Ensan is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Computer science & Static random-access memory. The author has an hindex of 5, co-authored 11 publications receiving 64 citations. Previous affiliations of Sina Sayyah Ensan include Sharif University of Technology.

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Journal ArticleDOI

A low-power single-ended SRAM in FinFET technology

TL;DR: A single-ended low-power 7T SRAM cell in FinFET technology enhances read performance by isolating the storage node from the read path by disconnecting the feedback path of the cross-coupled inverters during the write operation.
Journal ArticleDOI

A robust and low-power near-threshold SRAM in 10-nm FinFET technology

TL;DR: This paper presents a robust and low-power single-ended robust 11T near-threshold SRAM cell in 10-nm FinFET technology that eliminates write disturbance and enhances write performance by disconnecting the path between cross-coupled inverters during the write operation.
Journal ArticleDOI

A low-leakage and high-writable SRAM cell with back-gate biasing in FinFET technology

TL;DR: In this article, the authors proposed a 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 Fin-FET devices and increases the write static noise margin of the proposed cell due to their role in reducing the strength of the pull-down network of the cross-coupled inverters.
Proceedings ArticleDOI

FPCAS: In-Memory Floating Point Computations for Autonomous Systems

TL;DR: This work proposes F P arithmetic (adder/subtractor and multiplier) using Resistive RAM (ReRAM) crossbar based IMC and proposes a novel shift circuitry to lower the shift overhead inherently present in the FP arithmetic.
Journal ArticleDOI

ReLOPE: Resistive RAM-Based Linear First-Order Partial Differential Equation Solver

TL;DR: This brief proposes ReLOPE, a fully RRAM crossbar-based IMC to solve PDEs using the Runge–Kutta numerical method with 97% accuracy, and expands the operating range of solution by exploiting shifters to shift input data and output data.