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A Computing In-Memory Multibit Multiplication Based on Decoupling and In-Array Storing
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DOI：10.1109/TCSI.2024.3364715.png)
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Abstract
En 中文
Multiplications are basic operations of neural networks. Therefore, multiplication results are crucial in analyzing the operating process of neural networks. However, the multiplication strategies are generally based on analog-domain circuits, and the results are in a multiply-and-accumulate (MAC) form. The result of each multiplication in MAC cannot be distinguished accurately using these strategies. Therefore, we proposed an in-memory multibit multiplication based on the decoupling and in-array storage strategy to overcome this problem, and the core module is the 10T1C SRAM cell. Multibit multiplications are decoupled by a series of logical operations. Therefore, in the analysis mode, multiplication results can be saved and outputted in the normal read mode without requiring additional storage. When executing the neural network, the operation results are stored in the cells. Hence, the operands stored in the array are retained. Accumulation operations are completed based on the charge-sharing technology; thus, the linearity of accumulation is high. We simulated and analyzed the performance of the proposed circuit in a 28 nm CMOS process. The absolute value of integral nonlinearity is at most 0.29. Further, due to high data operation parallelism, the throughputs of the logical operation and MAC are up to 6307.8 and 802.8 GOPS, respectively.
Keywords:
Convolution
in-memory multibit multiplication
logical operation
multiply-and-accumulate
multiplication decomposition
neural network interpretability
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