Bridging the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Netowork Compiler
Authors: Yu Ji, Youhui Zhang, Wenguang Chen, Yuan Xie Venue: ASPLOS 2018 With the machine learning community trying to push the limits of neural networks on one hand, and the architecture community proposing their own constraints and data-flows to accelerate neural networks on the other hand, this paper tries to bridge the gap between the two communities by proposing a neural network compiler. The main aim of this paper is to run a given neural network on a given hardware, no matter what the constraints are. They achieve this by modelling the target NN as a computational graph, restructuring it based on the constraints of the target architecture, and fine-tuning the graph to minimise accuracy loss. One of the main conflicts between the NN and the hardware is the precision of inputs. This paper solves this issue by using an autoencoder network that produces the low-precision representation of the inputs. The accuracy loss incurred in translating to low-precision values can be...