  Meta Learning has been in focus in recent years due to the meta-learner
model's ability to adapt well and generalize to new tasks, thus, reducing both
the time and data requirements for learning. However, a major drawback of meta
learner is that, to reach to a state from where learning new tasks becomes
feasible with less data, it requires a large number of iterations and a lot of
time. We address this issue by proposing various acceleration techniques to
speed up meta learning algorithms such as MAML (Model Agnostic Meta Learning).
We present 3.73X acceleration on a well known RNN optimizer based meta learner
proposed in literature [11]. We introduce a novel method of training tasks in
clusters, which not only accelerates the meta learning process but also
improves model accuracy performance.
  Keywords: Meta learning, RNN optimizer, AGI, Performance optimization
