@inproceedings{bc39b29e79ff4c809797006e91afbc30,
title = "Hybrid Stochastic Computing of Linear Time O(N) and Its In-Memory Computing for High Performances",
abstract = "Stochastic computing (SC) reduces the complexity of arithmetic circuits but brings extra conversion cost and time complexity of O(2N), which leads to a much lower efficiency than binary. This paper proposes a linear-time, O(N), and conversion-free hybrid stochastic computing (HSC). Moreover, a hybrid stochastic computing in-memory method is proposed, mapping addition and multiplication of HSC into memory's enable and addressing circuits. Thus, the basic memory having enable and addressing circuits can realize HSC operation without additional circuits. The experiment shows that HSC in block memory (BRAM) based on FPGA for matrix multiplication reaches 2.304 TOPS (operation per second) and 17.2 TOPS/W.bit. Each 18K-BRAM provides 18 GOPS (INT8) with 8.34 mW at 600 MHz.",
keywords = "Computing in memory, High performance, Hybrid stochastic computing, Linear time complexity",
author = "Yuhao Chen and Hongge Li and Yinjie Song and Xinyu Zhu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2024 ; Conference date: 01-07-2024 Through 03-07-2024",
year = "2024",
doi = "10.1109/ISVLSI61997.2024.00146",
language = "英语",
series = "Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI",
publisher = "IEEE Computer Society",
pages = "753--756",
editor = "Himanshu Thapliyal and Jurgen Becker",
booktitle = "2024 IEEE Computer Society Annual Symposium on VLSI",
address = "美国",
}