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In-Memory Nearest Neighbor Search With Nanoelectromechanical Ternary Content-Addressable Memory
Cited 9 time in
Web of Science
Cited 10 time in Scopus
- Authors
- Issue Date
- 2022-01
- Citation
- IEEE Electron Device Letters, Vol.43 No.1, pp.154-157
- 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.
- ISSN
- 0741-3106
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