https://scholars.lib.ntu.edu.tw/handle/123456789/635959
標題: | Impact of Non-Volatile Memory Cells on Spiking Neural Network Annealing Machine With In-Situ Synapse Processing | 作者: | Wei, Ming Liang Yayla, Mikail Ho, Shu Yin Chen, Jian Jia Amrouch, Hussam CHIA-LIN YANG |
關鍵字: | Annealing | annealing machine | Capacitance | constraint satisfaction problems | Costs | Micromechanical devices | Neurons | non-volatile memory | Nonvolatile memory | Spiking neural network | Synapses | 公開日期: | 1-一月-2023 | 來源出版物: | IEEE Transactions on Circuits and Systems I: Regular Papers | 摘要: | Solving constraint satisfaction problems (CSPs) is in high demand for various applications. SNN serves as a competitive annealing machine that can solve the CSP more efficiently than well-known Metropolis sampling and Hopfield networks. NVM-based crossbars with analog Integrate and Fire (IF) neurons can evolve the state of SNN to solve CSP more efficiently. However, analog computations inherently suffer from imprecisions in NVM cells, e.g., current variation, OFF-state leakage, and temperature-induced drift. We are the first to analyze the impacts of various memory technologies, including 2T-NOR, FeFET, WOx ReRAM, and HfOx ReRAM, on solving the Ising model, Sudoku, and Traveling-salesman-problem (TSP). The results show that both 2T-NOR Flash and FeFET with normalized standard deviation( |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/635959 | ISSN: | 15498328 | DOI: | 10.1109/TCSI.2023.3305010 |
顯示於: | 資訊工程學系 |
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