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 a $\mathrm{n}\times \mathrm{n}\ (4\times 4)$ feature map requires $2^{n}-1$ (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.