  Recent developments in deep learning with application to language modeling
have led to success in tasks of text processing, summarizing and machine
translation. However, deploying huge language models for mobile device such as
on-device keyboards poses computation as a bottle-neck due to their puny
computation capacities. In this work we propose an embedded deep learning based
word prediction method that optimizes run-time memory and also provides a real
time prediction environment. Our model size is 7.40MB and has average
prediction time of 6.47 ms. We improve over the existing methods for word
prediction in terms of key stroke savings and word prediction rate.
