CiMComp: An Energy Efficient Compute-in-Memory based Comparator for Convolutional Neural Networks

The utilization of large datasets in applications results in significant energy expenditures attributed to frequent data shifts between memory and processing units. In-Memory Computing (IMC) distinguishes itself by employing computations within a memory crossbar to perform logic operations, leading to enhanced computational speed and energy efficiency. This study introduces RASA-based subtractor, strategically improved for computation, and energy consumption. Subsequently, the proposed subtractor are employed to construct a comparator and facilitate pooling operations. The comparator is developed using the proposed subtractor, achieves the comparison in n steps for a n-bit comparator. Additionally, a n-bit min pooling operation for anxn (4 x 4) feature map requires 2-1 (15) steps. Energy consumption of the RASA design demonstrates hopped up performance, showcasing an average savings of 87.42% and 89.98% compared to the ASA and Muller C based subtractor.