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- W4387449314 abstract "In-memory computing (IMC) is considered one of the most promising candidates to solve the non-traditional challenges conventional computing systems face in dealing with novel big data applications. This paper proposes an efficient hybrid MTJ/FinFET base in-memory computing (IMC) architecture performing all basic Boolean logic operations (AND/NAND, OR/NOR, XOR/XNOR) in only one system clock. To this end, a novel memory cell design based on in-plane magnetic tunnel junction (I-MTJ) is proposed, which can perform various logic operations in the memory array. In the proposed array, various logic operations can benefit from connecting I-MTJ memory cells in series in the selected column and a novel sense amplifier unit. Moreover, the full adder (FA) operation is accomplished by exploiting the majority logic function, which indicates its functionality mostly in a ripple carry adder (RCA) implementation that requires only n+2 clock cycles for n-bit calculation. The circuit-level simulations indicate that the proposed design improves energy consumption of performing the AND/NAND and Or/NOR operations by approximately 80%, the XOR/XNOR operations by 79%, and the FA operation by 47% compared to its state-of-the-art counterparts. Moreover, the Monte Carlo simulations authenticate the high robustness of the proposed architecture in the presence of process variations. To validate the proposed design’s efficiency in real-world applications, the minimum/maximum image filters, as the essential preprocessing steps in widely-used applications like Optical Character Recognition (OCR) and the VGG-16 neural network with the ImageNet dataset, are implemented using the proposed IMC architecture." @default.
- W4387449314 created "2023-10-10" @default.
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- W4387449314 date "2023-01-01" @default.
- W4387449314 modified "2023-10-11" @default.
- W4387449314 title "An Energy Efficient In-Memory Computing Architecture Using Reconfigurable Magnetic Logic Circuits for Big Data Processing" @default.
- W4387449314 doi "https://doi.org/10.1109/tmag.2023.3322731" @default.
- W4387449314 hasPublicationYear "2023" @default.
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