  Semantic segmentation remains a computationally intensive algorithm for
embedded deployment even with the rapid growth of computation power. Thus
efficient network design is a critical aspect especially for applications like
automated driving which requires real-time performance. Recently, there has
been a lot of research on designing efficient encoders that are mostly task
agnostic. Unlike image classification and bounding box object detection tasks,
decoders are computationally expensive as well for semantic segmentation task.
In this work, we focus on efficient design of the segmentation decoder and
assume that an efficient encoder is already designed to provide shared features
for a multi-task learning system. We design a novel efficient non-bottleneck
layer and a family of decoders which fit into a small run-time budget using
VGG10 as efficient encoder. We demonstrate in our dataset that experimentation
with various design choices led to an improvement of 10\% from a baseline
performance.
