SimuNN: A Pre-RTL Inference, Simulation and Evaluation Framework for Neural Networks

Abstract

Neural networks have been widely deployed in a number of applications due to their strong learning and feature extraction ability. To meet the ever increasing accuracy requirements from various applications, neural network models have become more complicated and diversified by exploiting more layers, larger model size, and more diverse functions. As a result, the design of application specified hardware accelerators for neural networks is also becoming more difficult, especially for real-time embedded applications. To efficiently bridge the gap between algorithm and hardware design phases, a pre-RTL neural network simulator (SimuNN) is proposed in this paper to enable early phase verification and fast prototyping. SimuNN can be used for inference, simulation and also evaluation, results of which can guide the design of both neural network models and hardware accelerators. SimuNN supports inference in various data precision, and is compatible with TensorFlow, which makes it easy for platform migration. It provides multi-level trace results that can be taken as the gold model of RTL designs. Moreover, the hardware performance under various quantizations, dataflow organizations and hardware configurations can be evaluated by SimuNN based on a generalized hardware model. Based on that, two dataflow organization schemes are concluded for determining the optimal configurations of hardware architecture under various hardware and performance constraints.