Inexact computing generally involves trading a reduction in accuracy for an improvement in circuit area and power-consumption. The multiply-accumulate (MAC) operation is used extensively in convolutional neural networks and such applications stand to benefit greatly from the introduction of approximation to the MAC operation. This paper introduces an unsigned approximate MAC unit architecture in which approximation is introduced to both the multiplication and accumulation stages. Four variations of the proposed design are implemented using the TSMC 65 nm technology and are used in an image smoothing application. The proposed architecture is compared to that of the exact MAC unit and is shown to reduce circuit area and power-consumption by 67% and 49% respectively. When compared to other approximate MAC architectures, the proposed design improves area-power product by up to 66%.