Core Viewpoint - The research highlights the significant energy consumption and area occupation of Analog-to-Digital Converters (ADC) in Compute-in-Memory (CIM) systems, which undermines the energy efficiency advantages that CIM technology promises [6][7]. Group 1: Background and Challenges - The AI wave has led to concerns over power consumption, particularly in traditional architectures where data transfers between CPU and memory are energy-intensive [3]. - CIM technology is seen as a potential solution to eliminate data transfer bottlenecks by performing calculations directly in memory [4]. - However, the necessity of ADC to convert analog signals back to digital introduces a significant energy and area cost, consuming up to 87% of total energy and 75% of chip area in advanced CIM systems [6][7]. Group 2: Limitations of Traditional ADC - Traditional ADCs use uniform quantization, which does not align with the diverse output signal distributions of neural networks, leading to precision loss [12]. - To compensate for this loss, designers often resort to higher precision ADCs, which results in exponential increases in power consumption and area, creating a vicious cycle [13]. Group 3: Innovative Solutions - The research team proposes using memristors to create adaptive quantization units (Q-cells) that allow for programmable quantization boundaries, enhancing the efficiency of ADCs [15][18]. - This adaptive quantization method significantly improves accuracy, with the VGG8 network achieving an accuracy of 88.9% at 4-bit precision, compared to 52.3% with traditional methods [21]. Group 4: System-Level Benefits - The new memristor-based ADC achieves a 15.1 times improvement in energy efficiency and a 12.9 times reduction in area compared to state-of-the-art designs [25]. - When integrated into CIM systems, the energy consumption of the ADC module in the VGG8 network drops from 79.8% to 22.5%, and area occupation decreases from 47.6% to 16.9%, leading to overall system energy savings of 57.2% [26][28]. - This innovation effectively addresses the ADC bottleneck in mixed-signal CIM systems, paving the way for more efficient and accurate next-generation AI hardware [30].