Convolutional neural networks (CNNs) have become a common solution for many Artificial Intelligence (AI) applications since they can achieve superior task accuracy compared to human experts or traditional algorithms. However, the billions of arithmetic steps of CNN processing require a computational effort and an amount of energy which do not match the tight power and area constraints of edge devices. Hence, it is common practice to perform CNN processing in data centers, with consistent overhead in terms of data movement, latency and power consumption. This work proposes the design of an accelerator featuring a reconfigurable approximate multiplier to compute 3D convolutions efficiently, making them implementable on power-constrained devices.