The invention relates to a
deep learning acceleration core, in particular to a front-end
simulation system and method for the
deep learning acceleration core, and a decoding module, which
decodes an external
test set according to different requirements of
software and hardware platforms; the hardware field
recovery module is used for determining a hardware
simulation starting point according to parameters input by a user, and recovering a hardware
simulation field to an initialized state when full-
process simulation needs to be carried out; when simulation needs to be started from the synchronization point of the
sync instruction, a hardware simulation site is recovered to a position close to the
sync instruction; the hardware simulation result output module is used for outputting the type of the current instruction and the corresponding program counting PC to a screen in real time when hardware simulation is carried out, and outputting a hardware simulation result; the
software simulator and the result
processing module are used for carrying out parameter configuration on the
software simulator and outputting a
software simulation result; according to the technical scheme provided by the invention, the defects of low efficiency and difficulty in quickly positioning error instructions in the prior art can be effectively overcome.