Deep learning algorithms are computationally intensive and require dedicated hardware accelerators. Deep learning algorithms repeat multiply-accumulate (MAC) operations. This process produces a large number of partial sums that account for about 60% of the total logic. Therefore, optimizing multi-operand adders (MOA) that add these partial sums can reduce the high resource utilization of deep learning accelerators. This study designed a neural processing element with approximate adders that reduces resource utilization without changing the accuracy of deep learning algorithms by using the fault tolerance property of deep learning algorithms. As a result, the accuracy dropped by only 0.04^% with 4.7% less resource usage.