Simulation acceleration of image filtering on CMOS vision chips using many-core processors

Autor: Tom J. Kazmierski, G. Domenech-Asensi
Přispěvatelé: University of Southampton (UK), Ministerio de Educación, Cultura y Deporte, Universidad Politécnica de Cartagena, Engineering and Physical Sciences Research Council, University of Southampton
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: FDL
Repositorio Digital de la Universidad Politécnica de Cartagena
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Popis: This paper describes an efficient numerical solution to speed up transient simulations of analog circuits on a many-core computer. The technique is based on an explicit integration method, parallelised on a multiprocessor architecture. Although the integration step is smaller than the required one by traditional simulation methods based on Newton–Raphson iterations, explicit methods do not require to compute complex calculations such us matrix factorizations, which lead to long CPU simulation times. The proposed technique has been implemented on a NVIDIA GPU and has been demonstrated simulating Gaussian filtering operations performed by a CMOS vision chip. These type of devices, which are used to perform computation on the edge, include built-in image processing functions, turning them into very complex and time consuming circuits during their design. The proposed method is faster that Ngspice for different image sizes, and for 128 x 128 pixels image size it achieves a speed up of two orders of magnitude. This work has been partially funded by Spanish government through project RTI2018-097088-B-C33 and by EPSRC (the UK Engineering and Physical Sciences Research Council) under grant EP/N0317681/1. The research stays at University of Southampton (UK) have been supported by Ministerio de Educación, Cultura y Deporte within the “Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, del Plan Estatal de I+D+I” under grant PRX18/00565 and by Universidad Politécnica de Cartagena - Campus de Excelencia Internacional Mare Nostrum
Databáze: OpenAIRE