Low Quantization Error Readout Circuit with Fully Charge-Domain Calculation for Computation-in-Memory Deep Neural Network.
IEEE International Symposium on Circuits and Systems(2024)
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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