Convolutional Neural Networks (CNNs) are widely used for machine learning tasks but often come with high computational costs due to their reliance on resource- intensive Multiply-ACcumulate (MAC) operations. As a more efficient alternative, AdderNet (AddNN) replaces these MAC operations with simpler Sum-of-Absolute- Difference (SAD) operations, employing an ℓ1-norm-based approach. While this architecture reduces computational expenses, it has not yet achieved the same level of hardware optimization as CNNs, particularly in areas such as effective quantization, accelerator design, and efficient use of FPGA resources like DSP slices. This thesis presents an efficient quantized implementation of the AddNN ResNet20 architecture using an 8-bit fixed-point quantization scheme.