Traditional hardware development exploits description languages such as VHDL and (System)Verilog to produce highly parametrizable RTL designs. Different parameter values yield different utilization-frequency trade-offs, and hand-tuning is not feasible with a non-trivial amount of parameters. Generally, the Computer-Aided Design (CAD) literature proposes approaches that mainly tackle automatic exploration without combining a design automation feature. Hence, this work proposes Dovado, an open-source CAD tool for design space exploration (DSE) tailored for FPGAs-based designs. Starting from VHDL/(System)Verilog, Dovado exploits Vivado and supports the hardware developer for an exact exploration of a given set of parameters or a DSE where it returns the non-dominated set of configuration points. In this work, we exploit a multi-objective integer formulation and Non-Dominated Sorting Genetic Algorithm (NSGA)-II for a fast DSE. Moreover, we propose an approximation model for the NSGA-II fitness function to decide whether Vivado or a Nadaraya-Watson model should estimate the optimization metrics.

Dovado: An Open-Source Design Space Exploration Framework

Conficconi, Davide;Santambrogio, Marco D.
2021-01-01

Abstract

Traditional hardware development exploits description languages such as VHDL and (System)Verilog to produce highly parametrizable RTL designs. Different parameter values yield different utilization-frequency trade-offs, and hand-tuning is not feasible with a non-trivial amount of parameters. Generally, the Computer-Aided Design (CAD) literature proposes approaches that mainly tackle automatic exploration without combining a design automation feature. Hence, this work proposes Dovado, an open-source CAD tool for design space exploration (DSE) tailored for FPGAs-based designs. Starting from VHDL/(System)Verilog, Dovado exploits Vivado and supports the hardware developer for an exact exploration of a given set of parameters or a DSE where it returns the non-dominated set of configuration points. In this work, we exploit a multi-objective integer formulation and Non-Dominated Sorting Genetic Algorithm (NSGA)-II for a fast DSE. Moreover, we propose an approximation model for the NSGA-II fitness function to decide whether Vivado or a Nadaraya-Watson model should estimate the optimization metrics.
2021
2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
978-1-6654-3577-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1179076
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