The invention discloses a
convolutional neural network accelerator based on a
lookup table and in-memory computation, which comprises an on-
chip main memory and a plurality of operation
processing units, and is characterized in that each operation
processing unit comprises a data preprocessing unit, an in-memory computation array unit, a first summator and an accumulator which are connected in sequence; serial input based on bit feature values is adopted, and an input feature value sequence is compressed; the data preprocessing unit is used for decompressing the compressed feature value sequence and the compressed weight, and
matrix multiplication based on a
lookup table form is carried out in the in-memory calculation array unit through an in-memory calculation
macro module; the first
adder is used for adding a first part and a result output by each column of in-memory computing
macro-module in the in-memory computing array unit to obtain a second part and a result, and the accumulator is used for calculating the second part and the result to obtain a multiply-accumulate result corresponding to the characteristic value
data stream; and high sparseness caused by unstructured
pruning can be effectively utilized.