  We propose a joint feature compression and transmission scheme for efficient
inference at the wireless network edge. Our goal is to enable efficient and
reliable inference at the edge server assuming limited computational resources
at the edge device. Previous work focused mainly on feature compression,
ignoring the computational cost of channel coding. We incorporate the recently
proposed deep joint source-channel coding (DeepJSCC) scheme, and combine it
with novel filter pruning strategies aimed at reducing the redundant complexity
from neural networks. We evaluate our approach on a classification task, and
show improved results in both end-to-end reliability and workload reduction at
the edge device. This is the first work that combines DeepJSCC with network
pruning, and applies it to image classification over the wireless edge.
