More than async

[Atry]

We are implementing asynchronous computing in DeepLearning.scala 2.0.
However, in order to maximize the throughput, we need on-device computing graph instead of CPU driven asynchronous computing.

In DeepLearning.scala 3.0, we will implement applicative-based computing graph, avoiding flatMap or map. We will keep a proper number of kernel for an on-device command queue, e.g. 3 kernels. Most of the on-CPU Futures await for command queue available, instead of awaiting for result.

• Counting the load of command queue
• Make tuples of Buffer and Event

[Atry]

Fixed in #36