We propose a novel real-time general purpose semantic segmentation architecture, called RGPNet, which achieves significant performance gain in complex environments. RGPNet consists of a light-weight asymmetric encoder-decoder and an adaptor. The adaptor helps preserve and refine the abstract concepts from multiple levels of distributed representations between encoder and decoder. It also facilitates the gradient flow from deeper layers to shallower layers. Our extensive experiments highlight the superior performance of RGPNet compared to the state-of-the-art semantic segmentation networks. Moreover, towards green AI, we show that using a modified label-relaxation technique with progressive resizing can reduce the training time by up to 60% while preserving the performance. Furthermore, we optimize RGPNet for resource-constrained and embedded devices which increases the inference speed by 400% with a negligible loss in performance. We conclude that RGPNet obtains a better speed-accuracy trade-off across multiple datasets.