A processor-implemented method of performing convolutional operations between a plurality of input activations and a plurality of weights in a neural network, each input activation of the plurality of input activations and each weight of the plurality of weights being represented by a respective multi bit expression, the method comprising: generating a bit vector having a plurality of bit values, each bit value corresponding to a respective input activation of the plurality of input activations and having either a first value or a second value, wherein generating the bit vector comprises generating bit values having the first value for input activations among the plurality of input activations whose values are not 0 and generating bit values having the second value for input activations among the input activations whose values are 0; merging the bit vector into the input activations such that each bit value of a plurality of bit values included in the bit vector is merged with a respective input activation by generating a merged multi bit expression for the respective input activation including the bit value as the most significant bit, MSB, and the multi bit expression of the respective input activation; merging the bit vector into the weights such that each bit value of the plurality of bit values included in the bit vector is merged with a respective weight by generating a merged multi bit expression for the respective weight including the bit value as the MSB and the multi bit expressions of the respective weight; applying a same sorting algorithm respectively to the merged multi bit expressions of the input activations and the weights to sort the input activations and the weights based on bits corresponding to the MSBs, wherein applying the sorting algorithm to the merged multi bit expressions of the input activations comprises grouping input activations whose value is 0; and implementing the neural network, including performing operations between the sorted input activations and the sorted weights, wherein the performing of the operations comprises skipping one or more of those operations for which the input activation is among the grouped input activations. generating a bit vector having a plurality of bit values, each bit value corresponding to a respective input activation of the plurality of input activations and having either a first value or a second value, wherein generating the bit vector comprises generating bit values having the first value for input activations among the plurality of input activations whose values are not 0 and generating bit values having the second value for input activations among the input activations whose values are 0; merging the bit vector into the input activations such that each bit value of a plurality of bit values included in the bit vector is merged with a respective input activation by generating a merged multi bit expression for the respective input activation including the bit value as the most significant bit, MSB, and the multi bit expression of the respective input activation; merging the bit vector into the weights such that each bit value of the plurality of bit values included in the bit vector is merged with a respective weight by generating a merged multi bit expression for the respective weight including the bit value as the MSB and the multi bit expressions of the respective weight; applying a same sorting algorithm respectively to the merged multi bit expressions of the input activations and the weights to sort the input activations and the weights based on bits corresponding to the MSBs, wherein applying the sorting algorithm to the merged multi bit expressions of the input activations comprises grouping input activations whose value is 0; and implementing the neural network, including performing operations between the sorted input activations and the sorted weights, wherein the performing of the operations comprises skipping one or more of those operations for which the input activation is among the grouped input activations.