RT info:eu-repo/semantics/conferenceObject
T1 Accelerating Scientific Model Optimization with a Pipelined FPGA-Based Differential Evolution Engine
A1 Castro Caballero, Manuel De
A1 Osorio, Roberto R.
A1 Torres de la Sierra, Yuri
A1 Llanos Ferraris, Diego Rafael
K1 Informática
K1 1203 Ciencia de Los Ordenadores
K1 3304 Tecnología de Los Ordenadores
AB Differential Evolution (DE) [5] with Numerical Integration (NI) is an ideal target for Custom Computing Machines on FPGAs, since it produces deep pipelines, requires minimal external memory bandwidth, and benefits from large memory bandwidth. DE is a genetic algorithm used for scientific model optimization. We propose a generic FPGA-based DE architecture, parameterized to accommodate to different scientific models. It supports both non-adaptive and adaptive NI methods. The core DE engine is programmed in VHDL for high adaptability and performance, whereas the scientific models and their NI are programmed in C++ for flexibility and easiness of development.
PB IEE
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/75777
UL https://uvadoc.uva.es/handle/10324/75777
LA eng
NO 33rd IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM), Fayetteville, Arkansas, USA
NO Producción Científica
DS UVaDOC
RD 19-jul-2025