As　an　important　task　in　scene　understanding,　semantic　segmentation　requires　a　large　amount　of　computation　to　achieve　high　performance.　In　recent　years,　with　the　rise　of　autonomous　systems,　it　is　crucial　to　make　a　trade-off　in　terms　of　accuracy　and　speed.　In　this　paper,　we　propose　a　novel　asymmetric　encoder-decoder　network　structure　to　address　this　problem.　In　the　encoder,　we　design　a　Separable　Asymmetric　Module,　which　combines　depth-wise　separable　asymmetric　convolution　with　dilated　convolution　to　greatly　reduce　computation　cost　while　maintaining　accuracy.　On　the　other　hand,　an　attention　mechanism　is　also　used　in　the　decoder　to　further　improve　segmentation　performance.　Experimental　results　on　CityScapes　and　CamVid　datasets　show　that　the　proposed　method　can　achieve　a　better　balance　between　segmentation　precision　and　speed　compared　with　state-of-the-art　semantic　segmentation　methods.　Specifically,　our　model　obtains　mean　IoU　of　72.5%　and　66.3%　on　CityScapes　and　CamVid　test　dataset,　respectively,　with　less　than　1M　parameters.