In-Memory Nearest Neighbor Search With Nanoelectromechanical Ternary Content-Addressable Memory
Jae Seong Lee, Jisoo Yoon, Woo Young Choi
Abstract
Nearest neighbor (NN) search is widely used in pattern classification and memory-augmented neural networks. To overcome the von Neumann bottleneck in conventional NN search architecture, in this study, nanoelectromechanical-switch-based ternary content-addressable memory (NEMTCAM) is introduced for the NN classifier. NEMTCAM can calculate the Hamming distance between the input vector and the stored vectors in a parallel search operation. The NEMTCAM operation was experimentally demonstrated. Furthermore, an analytical model for NN search accuracy, including cell-to-cell parasitic resistance, is presented. NEMTCAM can calculate up to 10 Hamming distances in 32-bit words owing to the high current ratio of the NEM memory switches.