With　the　rapid　increase　of　data,　stream　computation　contributes　to　the　improvement　of　data　real　time　processing.　One　of　characters　of　stream　computing　is　in　the　pipeline　to　exploit　parallelism　in　the　MIMD　way.　Moreover,　pipelined　parallel　implementation　is　a　common　problem　and　often　yields　much　better　parallel　efficiency　with　respect　to　other　commonly　used　methods.　Plentiful　physical　modeling　in　the　field　of　image　processing　and　machine　vision　deals　with　the　solution　of　partial　differential　equations,　typical　representative　by　Poisson’s　equation.　The　finite　difference　schemes　as　the　main　method　for　the　solution　of　partial　difference　equation　for　physical　modeling　can　be　time-consuming　and　computationally　expensive.　Constructing　highly　parallel　computation　models　can　greatly　solve　the　problem　and　it　is　important　that　quickly　and　efficiently　carry　out　it.　The　paper　mainly　studies　the　parallel　implementation　of　the　mix　of　Jacobi　iterative　and　Gauss-Seidel　method　for　the　finite　difference　schemes　to　solve　Poisson　equations　in　a　pipelined　fashion　on　FPGA.　©　2017　IEEE.