US 12,260,321 B2
Data feature augmentation system and method for low-precision neural network
Fu-Cheng Tsai, Tainan (TW); Yi-Ching Kuo, Tainan (TW); Chih-Sheng Lin, Tainan (TW); Shyh-Shyuan Sheu, Zhubei (TW); Tay-Jyi Lin, Kaohsiung (TW); and Shih-Chieh Chang, Hsinchu (TW)
Assigned to INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE, Hsinchu (TW)
Filed by INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE, Hsinchu (TW)
Filed on Jul. 26, 2021, as Appl. No. 17/385,316.
Claims priority of application No. 110111849 (TW), filed on Mar. 31, 2021.
Prior Publication US 2022/0318605 A1, Oct. 6, 2022
Int. Cl. H03M 1/12 (2006.01); G06N 3/04 (2023.01); G06N 3/063 (2023.01); G11C 27/02 (2006.01)
CPC G06N 3/063 (2013.01) [G06N 3/04 (2013.01); H03M 1/1245 (2013.01); G11C 27/02 (2013.01); H03M 1/12 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A data feature augmentation system for a low-precision neural network, comprising:
a first time difference unit, comprising:
a first sample-and-hold circuit, for receiving an input signal and obtaining a first signal according to the input signal, wherein the first signal is related to a first leakage rate of the first sample-and-hold circuit and the first signal is the signal generated by delaying the input signal by one time unit; and
a subtractor, for performing subtraction on the input signal and the first signal to obtain a time difference signal;
wherein the input signal and the time difference signal are inputted to the low-precision neural network.