Utilizing 2T SONOS Cell Characteristics for L2/Euclidean Distance Computing - From Unit Cell to Array Operations

This paper presents an innovative approach for in-memory Euclidean distance computation with 2T SONOS Flash memory as the computing unit. By utilizing the threshold voltage of Flash cells as stored data, and the input gate voltage as user query, the system can calculate the squared L2/Euclidean distance directly in the array based on the native response of transistor physics, which offers significant advantages in terms of energy efficiency and speed. We introduce an optimized reference current extraction technique, which enhances the accuracy on L2 distance computation despite the non-ideal factors from memory devices. The impacts of Flash device variations and threshold voltage distribution on the recognition accuracy are considered, which further demonstrates the system's robustness against such variations. Simulations showed that the proposed system achieves a recognition accuracy of 94.82% with the Google's FaceNet model and the CelebA dataset, which is comparable to the results with CPU-computed L2 and the cosine-similarity algorithms. The proposed approach highlights the potential of Flash memory-based in-memory computing as an efficient solution for high-accuracy, distance-based applications.