US 12,079,708 B2
Parallel acceleration method for memristor-based neural network, parallel acceleration processor based on memristor-based neural network and parallel acceleration device based on memristor-based neural network
Huaqiang Wu, Beijing (CN); Peng Yao, Beijing (CN); Bin Gao, Beijing (CN); and He Qian, Beijing (CN)
Assigned to TSINGHUA UNIVERSITY, Beijing (CN)
Appl. No. 17/049,348
Filed by TSINGHUA UNIVERSITY, Beijing (CN)
PCT Filed Jan. 10, 2020, PCT No. PCT/CN2020/071424
§ 371(c)(1), (2) Date Oct. 21, 2020,
PCT Pub. No. WO2021/088248, PCT Pub. Date May 14, 2021.
Claims priority of application No. 201911082236.3 (CN), filed on Nov. 7, 2019.
Prior Publication US 2022/0335278 A1, Oct. 20, 2022
Int. Cl. G06N 3/063 (2023.01); G06F 9/30 (2018.01); G06F 9/345 (2018.01); G06F 9/38 (2018.01)
CPC G06N 3/063 (2013.01) [G06F 9/30036 (2013.01); G06F 9/345 (2013.01); G06F 9/3877 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A parallel acceleration method for a memristor-based neural network, wherein the memristor-based neural network comprises a plurality of functional layers sequentially provided, the plurality of functional layers comprise a first functional layer and a second functional layer following the first functional layer, the first functional layer comprises a plurality of first memristor arrays in parallel, the plurality of first memristor arrays are configured to independently execute an operation of the first functional layer in parallel and to output a plurality of results of the operation to the second functional layer, weight parameters of the memristor-based neural network comprise a plurality of weight parameters of the first functional layer, and the plurality of weight parameters of the first functional layer are written into the plurality of first memristor arrays in one-to-one correspondence, respectively, so as to determine conductances of the plurality of first memristor arrays, and
the parallel acceleration method comprises:
splitting an input data received by the first functional layer into a plurality of sub-input data in one-to-one correspondence with the plurality of first memristor arrays; and
independently executing the operation of the first functional layer on the plurality of sub-input data in parallel via the plurality of first memristor arrays, so as to correspondingly generate a plurality of sub-operation results, wherein the plurality of results of the operation comprises the plurality of sub-operation results,
wherein the weight parameters of the memristor-based neural network further comprise a plurality of weight parameters of functional layers other than the first functional layer, and the plurality of weight parameters of the functional layers other than the first functional layer are written into memristor arrays corresponding to the functional layers other than the first functional layer, so as to determine conductances of the memristor arrays corresponding to the functional layers other than the first functional layer.