STONNE: A Detailed Architectural Simulator for Flexible Neural Network Accelerators
This provides a tool for researchers in computer architecture to explore and evaluate accelerator designs, though it is incremental as it builds on existing simulation and architectural concepts.
The authors tackled the need for efficient simulation tools for flexible neural network accelerators by presenting STONNE, a cycle-accurate simulator that models architectures like MAERI and reveals low compute unit utilization (25% on average across 5 DNN models), leading to poor performance.
The design of specialized architectures for accelerating the inference procedure of Deep Neural Networks (DNNs) is a booming area of research nowadays. First-generation rigid proposals have been rapidly replaced by more advanced flexible accelerator architectures able to efficiently support a variety of layer types and dimensions. As the complexity of the designs grows, it is more and more appealing for researchers to have cycle-accurate simulation tools at their disposal to allow for fast and accurate design-space exploration, and rapid quantification of the efficacy of architectural enhancements during the early stages of a design. To this end, we present STONNE (Simulation TOol of Neural Network Engines), a cycle-accurate, highly-modular and highly-extensible simulation framework that enables end-to-end evaluation of flexible accelerator architectures running complete contemporary DNN models. We use STONNE to model the recently proposed MAERI architecture and show how it can closely approach the performance results of the publicly available BSV-coded MAERI implementation. Then, we conduct a comprehensive evaluation and demonstrate that the folding strategy implemented for MAERI results in very low compute unit utilization (25% on average across 5 DNN models) which in the end translates into poor performance.