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초록
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.
키워드
Nanoelectromechanical systems; Artificial neural networks; Nearest neighbor methods; Integrated circuit modeling; Resistance; Nanoscale devices; Transistors; Ternary content-addressable memory (TCAM); nearest neighbor search; memory-augmented neural network (MANN); nanoelectromechanical (NEM) memory switch; CMOS-NEM hybrid circuit
- 제목
- In-Memory Nearest Neighbor Search With Nanoelectromechanical Ternary Content-Addressable Memory
- 저자
- Lee, Jae Seong; Yoon, Jisoo; Choi, Woo Young
- 발행일
- 2022-01
- 유형
- Article
- 권
- 43
- 호
- 1
- 페이지
- 154 ~ 157