The invention provides a neural network training method and device, equipment and a medium, and the method comprises the following steps: 1, obtaining training data of an image, voice or text type, and carrying out the normalization and
feature extraction processing; according to the invention, the three-terminal two-dimensional photoelectric
synapse device is prepared, a single / double input mode is supported, cooperative regulation and control of an optical
pulse sequence and a grid
electric signal sequence are realized, the problem that the multi-mode
processing capability of a traditional double-terminal device is limited is solved, and the complex
feature extraction efficiency is improved; the nonvolatile storage of
synaptic weight is realized through the ferroelectric regulation and
control layer, and the remanent polarization of the ferroelectric regulation and
control layer is Prgt; 5 [mu] C / cm < 2 > and erasing durability gt; 106 cycles are carried out, continuous power supply is not needed, and training
power consumption and cost are reduced; through the high carrier mobility (gt, 200cm < 2 > / Vs) and
nanosecond-level photoresponse characteristics (
carrier lifetime lt, 10ns) of the two-dimensional photosensitive layer (such as MoS2 / WSe2), the response speed of the device is increased to
nanosecond level, and the training requirement of a high-speed neural network is met.