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Programmable In-Memory Computing Circuit for Solving Combinatorial Matrix Operation in One Step
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DOI：10.1109/TCSI.2023.3263217.png)
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Abstract
En 中文
Matrix operations are widely used in practical engineering, but the traditional processing methods rely on the loop iterations and neural network algorithm on the software, requiring a long time to calculate. To address such problem, this paper proposes full hardware in-memory computing circuits based on programmable memristor unit array that can solve combinatorial matrix operations of any order in just one step. First, two basic circuit modules are introduced, which can respectively solve matrix multiplication and matrix equation. Further, the basic modules can be linked to solve combinatorial matrix operations with different forms. It's worth noting that every module can parallel program the value of each memristor in the memristor unit array and complete one-step computation by hardware. Then, some matrix operations are given in the paper as examples to prove the high accuracy of proposed method, where the average accuracy rate achieves 99%. The PSPICE simulation results demonstrate that the processing speed is improved enormously according to the comparison of hardware and software. Moreover, the proposed method has broad application prospect in practical engineering, such as using designed combinational circuit to solve domain shift problem in zero-shot learning, which greatly accelerates the training process of zero-shot learning.
Keywords:
Memristors
Mathematical models
Hardware
Threshold voltage
Computational modeling
Integrated circuit modeling
In-memory computing
Memristor
matrix operation
circuit design
Sylvester equation
zero-shot learning
Journal
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5.2
Papers:
9.7K
Citations:
2.2W
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