  This paper presents, NeuroTrainer, an intelligent memory module with
in-memory accelerators that forms the building block of a scalable architecture
for energy efficient training for deep neural networks. The proposed
architecture is based on integration of a homogeneous computing substrate
composed of multiple processing engines in the logic layer of a 3D memory
module. NeuroTrainer utilizes a programmable data flow based execution model to
optimize memory mapping and data re-use during different phases of training
operation. A programming model and supporting architecture utilizes the
flexible data flow to efficiently accelerate training of various types of DNNs.
The cycle level simulation and synthesized design in 15nm FinFET showspower
efficiency of 500 GFLOPS/W, and almost similar throughput for a wide range of
DNNs including convolutional, recurrent, multi-layer-perceptron, and mixed
(CNN+RNN) networks
