Title: AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks,
Abstract: New types of machine learning hardware in development and entering the market
hold the promise of revolutionizing deep learning in a manner as profound as
GPUs. However, existing software frameworks and training algorithms for deep
learning have yet to evolve to fully leverage the capability of the new wave of
silicon. We already see the limitations of existing algorithms for models that
exploit structured input via complex and instance-dependent control flow, which
prohibits minibatching. We present an asynchronous model-parallel (AMP)
training algorithm that is specifically motivated by training on networks of
interconnected devices. Through an implementation on multi-core CPUs, we show
that AMP training converges to the same accuracy as conventional synchronous
training algorithms in a similar number of epochs, but utilizes the available
hardware more efficiently even for small minibatch sizes, resulting in
significantly shorter overall training times. Our framework opens the door for
scaling up a new class of deep learning models that cannot be efficiently
trained today.