An apparatus for performing forward computation of an artificial neural network, the apparatus comprising a master operation unit, one or more slave operation units, and a H tree unit between the master operation unit and the slave operation units, wherein, the master operation unit is configured to transmit an input neuron vector of a layer in the artificial neural network to each slave operation unit; the H tree unit is configured to route the input neuron vector stored in the master operation unit to each slave operation unit at the beginning stage of the current layer of artificial neural network; the multiple slave operation units are configured to perform a dot product operation in parallel on the input neuron vector transmitted from the master operation unit and different weight vectors of corresponding slave operation unit respectively to obtain intermediate results, and then return the intermediate results to the master operation unit; characterized in that the master operation unit is further configured to cache the input neuron vector into a cache of each slave operation unit, the H tree unit is configured to splice the intermediate results of each slave operation unit into a complete intermediate result vector; and the master operation unit is also configured to perform an activation operation on the intermediate result vector to obtain a final output neuron vector; the apparatus further comprises a direct memory access unit configured to write data into corresponding cache units of the master operation unit and the respective slave operation units from an external address space or read data from the cache units to the external address space; an instruction cache unit configured to read instructions via the direct memory access unit and cache the instructions; and a controller unit configured to read instructions from the instruction cache unit, decode the instructions into micro instructions for controlling the master operation unit, the slave operation units and the direct memory access unit. the master operation unit is configured to transmit an input neuron vector of a layer in the artificial neural network to each slave operation unit; the H tree unit is configured to route the input neuron vector stored in the master operation unit to each slave operation unit at the beginning stage of the current layer of artificial neural network; the multiple slave operation units are configured to perform a dot product operation in parallel on the input neuron vector transmitted from the master operation unit and different weight vectors of corresponding slave operation unit respectively to obtain intermediate results, and then return the intermediate results to the master operation unit; characterized in that the master operation unit is further configured to cache the input neuron vector into a cache of each slave operation unit, the H tree unit is configured to splice the intermediate results of each slave operation unit into a complete intermediate result vector; and the master operation unit is also configured to perform an activation operation on the intermediate result vector to obtain a final output neuron vector; the apparatus further comprises a direct memory access unit configured to write data into corresponding cache units of the master operation unit and the respective slave operation units from an external address space or read data from the cache units to the external address space; an instruction cache unit configured to read instructions via the direct memory access unit and cache the instructions; and a controller unit configured to read instructions from the instruction cache unit, decode the instructions into micro instructions for controlling the master operation unit, the slave operation units and the direct memory access unit.