Publication: STONNE: enabling cycle-level microarchitectural simulation for DNN Inference accelerators
Authors
Muñoz Martínez, Francisco ; Abellán Miguel, José Luis ; Acacio Sánchez, Manuel Eugenio ; Krishna, Tushar
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Facultad de Informática
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Publisher
IEEE
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DOI
https://doi.org/10.1109/IISWC53511.2021.00028
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info:eu-repo/semantics/conferenceObject
Description
Abstract
The design of specialized architectures for accelerating the inference procedure of Deep Neural Networks (DNNs) is a booming area of research nowadays. While first-generation rigid accelerator proposals used simple fixed dataflows tailored for dense DNNs, more recent architectures have argued for flexibility to efficiently support a wide variety of layer types, dimensions, and sparsity. As the complexity of these accelerators grows, the analytical models currently being used for design-space exploration are unable to capture execution-time subtleties, leading to inexact results in many cases as we demonstrate. This opens up a need for cycle-level simulation tools to allow for fast and accurate design-space exploration of DNN accelerators, 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-level microarchitectural simulation framework that can plug into any high-level DNN framework as an accelerator device and perform full-model evaluation (i.e. we are able to simulate real, complete, unmodified DNN models) of state-of-the-art rigid and flexible DNN accelerators, both with and without sparsity support. As a proof of concept, we use STONNE in three use cases: i) a direct comparison of three dominant inference accelerators using real DNN models; ii) back-end extensions and iii) front-end extensions of the simulator to showcase the capability of STONNE to rapidly and precisely evaluate data-dependent optimizations.
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Citation
F. Muñoz-Martínez, J. L. Abellán, M. E. Acacio and T. Krishna, "STONNE: Enabling Cycle-Level Microarchitectural Simulation for DNN Inference Accelerators," 2021 IEEE International Symposium on Workload Characterization (IISWC), Storrs, CT, USA, 2021, pp. 201-213, doi: 10.1109/IISWC53511.2021.00028
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