INFERENCE FOCUS FOR OFFLINE TRAINING OF SRAM INFERENCE ENGINE IN BINARY NEURAL NETWORK

• 1. 用于神经网络推理的芯片及加速神经网络推理的方法 [P] . 中国专利： CN113138957A . 2021-07-20
• 2. 推理车道的方法、训练车道推理模型的方法及装置 [P] . 中国专利： CN113963325A . 2022-01-21
• 3. KERNEL TRANSFORMATION TECHNIQUES TO REDUCE POWER CONSUMPTION OF BINARY INPUT, BINARY WEIGHT IN-MEMORY CONVOLUTIONAL NEURAL NETWORK INFERENCE ENGINE [P] . WO2021126294A1 . 2021-06-24

  机译： 内核变换技术降低二进制输入功耗，二进制重量卷积神经网络推理引擎

• 4. KERNEL TRANSFORMATION TECHNIQUES TO REDUCE POWER CONSUMPTION OF BINARY INPUT, BINARY WEIGHT IN-MEMORY CONVOLUTIONAL NEURAL NETWORK INFERENCE ENGINE [P] . US2021192325A1 . 2021-06-24

  机译： 内核变换技术降低二进制输入功耗，二进制重量卷积神经网络推理引擎

• 5. Electronic device and method for inference Binary and Ternary Neural Networks [P] . KR20210059623A . 2021-05-25

  机译： 推理二进制和三元神经网络的电子设备和方法

• 1. Column breakthrough studies for the removal and recovery of phosphate by lime-iron sludge:Modeling and optimization using artificial neural network and adaptive neuro-fuzzy inference system [J] . Beverly S. Chittoo, Clint Sutherland 中国化学工程学报（英文版） . 2020,第007期
• 2. Optimizing deep learning inference on mobile devices with neural network accelerators [J] . Zeng Xi, Xu Yunlong, Zhi Tian 高技术通讯（英文版） . 2019,第004期
• 3. Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system [J] . Rafie Meraj, Samimi Namin Farhad 矿业科学技术（英文版） . 2015,第004期
• 4. Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system [J] . Rafie, Meraj, Samimi, 矿业科学技术学报：英文版 . 2015,第004期
• 5. XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference [J] . Francesco Conti, Pasquale Davide Schiavone, Luca Benini IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems . 2018,第11期
• 6. POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees [J] . Abdelsalam Ahmed M., Elsheikh Ahmed, Chidambaram Sivakumar, Journal of VLSI signal processing . 2020,第1期
• 7. An interpretation of neural networks as inference engines with application to transformer failure diagnosis [J] . Adriana R. Garcez Castro, Vladimiro Miranda International Journal of Electrical Power & Energy Systems . 2005,第9a10期
• 8. A Hybrid RRAM-SRAM Computing-In-Memory Architecture for Deep Neural Network Inference-Training Edge Acceleration [C] . Jiayun Feng, Yu Wang, Xianwu Hu, Silicon Nanoelectronics Workshop . 2021
• 9. Caffeinated FPGAs: FPGA Framework for Training and Inference of Convolutional Neural Networks With Reduced Precision Floating-Point Arithmetic [D] . DiCecco, Roberto. 2018
• 10. Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks [O] . Juexin Wang, Anjun Ma, Qin Ma, 2020
• 11. XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference [O] . Francesco Conti, Pasquale Davide Schiavone, Luca Benini 2018

• 1. Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference [J] . Evans Nyasha Chogumaira ,Takashi Hiyama . 能源与动力工程（英文） . 2011,第1期
• 2. 假设推理中多推理者的推理机制研究 [C] . 王献昌 . 第一届中国人工智能联合学术会 . 1990
• 3. 基于神经网络推理机的复杂Job-shop排序模型优化算法 [A] . 潘郁 . 2007