Low Quantization Error Readout Circuit with Fully Charge-Domain Calculation for Computation-in-Memory Deep Neural Network.

Ao Shi, Yizhou Zhang, Lixia Han, Zheng Zhou, Yiyang Chen, Lifeng Liu, Linxiao Shen, Peng Huang, Xiaoyan Liu, Jinfeng Kang

IEEE International Symposium on Circuits and Systems(2024)

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摘要
This work presents a low quantization error readout circuit with fully-charge-domain calculation for quantization and post-process of computation-in-memory (CIM)-based neural network. The contributions include: (1) A novel residual charge accumulation function is designed to achieve charge-domain summation of quantized partial sum, and reduces 38% quantization error; (2) Charge reset is introduced in the integrate & fire circuit to realize <1 LSB INL at ±7 bits and speed of 285MHz/LSB; (3) Sample & hold, current subtraction and bidirectional counter are designed to improve 3.95× energy efficiency and 2.48× area efficiency.
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关键词
computation in memory,ADC,deep neural network
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