Cross-Layer Reliability Evaluation of In-Memory Similarity Computation

The Memory Wall represents a significant performance and energy bottleneck in conventional computer architecture, caused by the frequent and costly data transfers between memory and processor cores. Computation in Memory (CiM) offers a promising solution, particularly benefiting similarity computation—a key component in various computer science applications—through the use of emerging non-volatile memory (NVM) technologies. However, intrinsic non-idealities of NVM technologies coupled with noise-sensitivity of analog circuitry can impair the reliability and correct functionality of the applications. This paper performs a cross-layer technology to application reliability and performance analysis of NVM-based similarity computation in memory. We consider various NVM technologies, different CiM-based similarity computation modules, and different real-world applications that are accurately simulated in a full-system environment. The results demonstrate significant improvements in both latency (up to 12x) and energy efficiency (up to 6.7x) of NVM-CiM compared to traditional architectures. Importantly, the study finds that latency and energy are largely unaffected by the specific NVM technology used, though reliability varies significantly—up to 28% in terms of architectural vulnerability factor (AVF).