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DSP的问题,ccs编译器中乘法与加法的代码运行时间问题 sos
在ccs中编译了一个简单的加法语句,仿真测得花费时间为2个时钟周期;简单的乘法语句也是花费2个时钟周期;移位运算,有的花费2个tclk,有的花费4个甚至更多; 使用的是DSP5509芯片,自带双乘法器和双加法器,所以DSP中运行一个加法,不是应该花费一个时钟周期嘛? 不太懂,希望有大佬可以指点一下学习。
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DSP280049远程帧为什么寄存器没有ID的? sos
我使用远程帧自动应答模式但是CAN_IF2ARB寄存器中ID全为0,但是改成数据帧这个寄存器里却会存在正确的ID号,怎么回事呢 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3EALABrnjHgvuDPZ6ma5yUScMrVSvKRyc5Nfk1wiBJLmtkA6wyVjfY2zpbVE/hU7ZbtVv9QgNfg/LMSVd0aX0SRblyiwEQKlAGrLlTGhowhGfJSJQmPx35i+y6BxM/lJ/naT+LWavTRIdQPd60AgAVgMRZQAAVQYFYFugDWrBWiMBSYQAEAC8CawKzIEgVQAAXyCgBYeW0IWY8CABaAtR5rpiUogAKLUADAWkQ3UckLFQCwAKwLTYjkKIACKNBPAQCrn17EXqYCABaAtUzLvajWbYfQgyeILiqHxCiAApoCAJamCn5rUwDAArDWZtO0BwVQ4MYVALBuvIOo3igKAFgDActOELzRABvABrABbAAbwAY0GwCwBgLWKHhLJiiAAiiwQQXslxEvFFi7AgAWgLV2G6d9KIACN6YAgHVjHUJ1JlEAwAKwJjEsMkUBFECBnAIAVk4Z/NekAIAFYK3JnmkLCqDAAhQAsBbQSVTxYgUALADrYiMiAxRAARToowCA1Uct4i5VAQALwFqq7VJvFECBhSoAYC2046h2LwUALACrl8HcVOS7o9nvdma325n98a69aqdDFXe325su0dszTGPIv88fTmmY95F6FyP52GeHq3/fZOcMcKHAbSjQBbDujns3XusxvsPwb6PzqEVnBQAsAKuzsVwS8fGPP5sXXn7dZ/HtD0/Mq298aKy/fbWF+4Shw4HKfr83u/3RtCGWwM+UgGUE4gpfBvLFUYgStvLsBrDOWuBatAJtgFWP1eBGSG5KOozzRQtD5VelAIAFYM1i0L/8+pt56ZV3fFlv3vu8cr/9waPqsy3cJwwdAliHQ7WSVQSWKu7eHA72rjiYuMP8RnHL3/Dk/m7nzhz39o48F16oBIBVEIegJSlQBKycnef8l9Rw6ropBQAsAGs2gxfA+v2Pv8wnj76vyn3/wZe+/LZwH1EcAljHO1Pd8Rbubqvww8nUq0dTApap67LbGRX45E5cDZSGZT75gskIg/fSFCgBVr16pd2AuJuXIWNnaQJR31UoAGABWLMZsgCULfDeR19X5b57/wtfflu4jyiOALDqrbkMOLl4dl7OAZZs29nzXPatzeH1xO/Og9h4Fug06HF+2pkRKSfJX9K48tU6aGWJFnyiwIIUyAOWW+FVb5ZKYQtqPFXdjAIAFoA1m7GHAHX/s5N57a2PzVffPfblt4X7iOIIAcu4yTchF4Gq+o5YA6wanEI4uzPHQ3imS7b1wjjnlaoUhiR+fBee8bftiOqd1snYArPwJ5LwiQJLUCAPWOVVqvzq1hJaTR23pgCABWBdzeb/fv68WHZbuGkAloBUE4KMqSdsecowBazyhG4rmF11qphHX/FS07j6aitbiRBaXAArkQmPZSqQBSzN7oMmAliBGDhvXgEAC8CaxUj7PjUYP1WoVjICrBi4bJoYqOJrAbD8oXO36qRuWUj+ypai8kWhQpfWMElrtwvDlS0AS1MLvwUqAGAtsNOocm8FACwAq7fRDEnQ96nB+KlCtcwYsPyKkmzNpatTKWDJtl29EiUrXefy0jzOYaVtO8lX6lICNVdGcP5KzoIBWA21uViJAlnAcivODbsP2swKViAGzptXAMACsGYx0iFPDYZnstRKKoAl55QqUFJWfFLAsjmngHMGraGAFa1uaXWtGiUgZgEv3N5UylXao+qCJwrcuAKtgKWuGJduUm68wVRvkwoAWADWLIY/5KnBQYAlh933R3M8WGiRFaS6mTpgiQQhaAnsOD91wo8gSrKRT9nqK/08RBBHktWfAFZTD67WpEAesOThkea4zY6JNYlCW1anAIAFYM1i1EOeGhwGWGfosdts55WoupllwArjnM9VqU/0VVHPq0/hUamzoBK+N3v746IapOUAy61WNbZKWME6S4tr0QqUAEtWoZMxVdm/3PgsuvlUfiMKAFgA1mym3vZUYFt4UtHctptAS2PLrU6dAtbJHKKZPD3nIStb4V21wJP+FKHUtS4vd77LxpJ8grx9/TnkLjryuS4FioBVGhPRWF2XKrRmbQoAWADWcm06B1hy2F2ZjFPAOh9U9wfLtZWm3DmttlUlD0ulO28BuBrE6m1NtgiXa5jUvE2BMmDVqesbHRkT5xXltrwJR4FbUQDAArBuxRYXWQ8V2BbZEiqNAvMp0AWw5qsNJaHANAoAWADWNJa1iVzd9p664rUJAWgkCgxSAMAaJBuJFqYAgAVgLcxkr1Bduw2YQJScnSpt/V2hrhSJAgtQAMBaQCdRxYsV2ARg/d//fGfa3n9GoPXw4UOj+T345idj37w2poA7a+XPacW/sr4xOWguClyiAIB1iXqkXYoCmwGsv//7vyb3tvClwZTmB2AtxbSpJwqgwK0qAGDdas9QrzEVALD++7/V6pYGU5ofgDWm+ZEXCqDAFhUAsLbY69trM4A1ELDsBMEbDbABbAAbwAawAWxAs4HNABZnsLZ390CLUQAFblMB+2XECwXWrsAmACve6utyzSH3tZs+7UMBFLiWAgDWtZSn3DkV2ARgPX361LS9Y+gCsOY0Q8pCARTYkgIA1pZ6e7tt3QxgPXv2zOTeFr4ArO0OAlqOAigwrwIA1rx6U9p1FACwnj2rVrcArOsYIKWiAApsTwEAa3t9vsUWA1gA1hbtnjajAApcUQEA64riU/RsCmwGsDiDNZtNzVPQ3dHs7a+p7/J/VXM62PCd2R/vLquT+xX3w+mybEiNAihQK9AFsOo/Uq/HcPUPCgxAzGdhCmwCsOLtvy7XHHIf15If//izeeHl132m3/7wxLz6xofG+ttXW7hPGDj8BKxNvPLXNlFYDV0H04uVAKxAdZwocLkCbYBVj9Pg5kluqJL/BL28LuSAAlMpAGBF/0Eo8AVgjWtyv/z6m3nplXd8pm/e+7xyv/3Bo+qzLdwnbDjkD5d3pslR4h9M0FU68QewGjJygQIzK1AErNwNTc5/5rpTHAp0VQDAArC62srF8QSwfv/jL/PJo++r/N5/8KXPty3cRwwdyp2trGylW4MAVigdbhS4lgIlwMqvMp/MgT9Zv1aXUe4ABQAsAGuA2QxLIgBlU9/76Osqk3fvf+Ezawv3ESNHA6gU4LLRJU51lqM6u6Wfz5JzWxKvgjTunCPFuUSByxTIA5a7CVK3Akthl9WH1CgwhQIAFoA1hV2peYYAdf+zk3ntrY/NV9899nHbwn3ExCErU3uz35cOvks8bYtQwprbiha49vt9dVi+uQ2ZVAIPFECBjgrkAau8SpVf3epYMNFQYEYFACwAa0Zzaxb19/PnTY/oqi28Ed2tMpWfGhSISgFLVrg0iJJVLS2sUQcuUAAFOimQBSxZgc4MNgCrk7xEuhEFACwAaxZT7PvUYPxUYWslA8DaZSZnY3KA5fzVbQljDFuErfITAQX6KABg9VGLuEtVAMACsGax3b5PDcZPFZYr6bYVdgdzqH77qrnNd06bA6zytgSAdVYQFwqMoUAWsEx5LLKCNYb65DGXAgAWgDWLrQ15ajA8k1WqZGMLT7YY1NUoAKukI2EoMJcCrYBVGr9q2Fw1pxwU6K4AgAVgdbeWC2IOeWqwE2DJ1mCwLSjnqbr/TIO7a85M3JJfUMQFSpAUBVAgD1h2R94+qJKekzQtq1uoigK3pgCABWDNYpNDnhpsB6zz1mDzl9llpSreKhT/dPKuJ/U4vpVG0sQ/ZjqLbBSCAqtUoARY2S356mZKG6OrlIhGrUABAAvAms2M254KbAuPK9rYGkwDq59W2EWrUvnVKA3WarjiZxpicblGgcsUKAKWv6kJboRk659l5MuEJ/WsCgBYANasBjdWYQJX+ScGZathZ5pxzitS6U86CGSd/2C2ms95inCsbiMfFKgUKANWLZIf4+6HgWErjGdpCgBYANbSbJb6ogAKLFyBLoC18CZSfRQwABaAxTBAARRAgVkVALBmlZvCrqQAgAVgXcn0KBYFUGCrCgBYW+35bbUbwAKwtmXxtBYFUODqCgBYV+8CKjCDAgAWgDWDmVEECqAACpwVALDOWuBarwIAFoC1XuumZSiAAjepAIB1k91CpUZWAMAaCFh2guCNBtgANoANYAPYADag2QCANRCwRgZdskMBFECBzShgv4x4ocDaFQCwAKy12zjtQwEUuDEFAKwb6xCqM4kCABaANYlhkSkKoAAK5BQAsHLK4L8mBQAsAGtN9kxbUAAFFqAAgLWATqKKFysAYAFYFxsRGaAACqBAHwUArD5qEXepCgBYANZSbZd6owAKLFQBAGuhHUe1eykAYAFYvQzmpiLfHc1+tzM77X04Zat6d9xXafbHu2wcAlAABaZToAtgyTj147swpqerKTmjwHAFACwAa7j19Ej5+MefzQsvv+5TfPvDE/PqGx8a629fbeE+YehwgKWB0ulQg5cWJhO3FhZmjxsFUGAaBdoAqx6/e+PvgeRman803BZN0yfkOr4CABaANb5VKTn+8utv5qVX3vEhb977vHK//cGj6rMt3CcMHQXAqqLJpMydb6gabhS4ugJFwDodqhXmZNjm/K/eGiqAAroCABaApVvGBL4CWL//8Zf55NH3VQnvP/jSl9QW7iOKow2wjDGyWpVM1pIHnyiAArMrUAKsevXqYNJN/pM52OMADObZ+4sChykAYAFYwyxnQCoBKJv03kdfVzm8e/8Ln1NbuI8ojg6AZYwyKWt3wt7vzhz37lwX2xGiNJ8oMKoCecBy408de6WwUatHZigwigIAFoA1iiF1ySQEqPufncxrb31svvrusU/aFu4jiqMTYAkwBXfEHqYkI2OM89vv94azWYEuOFFgAgXygKXcEAXl51e3gkg4UeBGFACwAKyrmeLfz58Xy24LN50Ay7KTXZHqBlg79c65WE0CUQAFeiqQBSw3pnPbgMlY7lku0VFgTgUALABrFnvr+9Rg/FShWskJAIvVK1VpPFFgVAUArFHlJLMbVWATgPX06VPT9v4zAq2HDx8aze/BNz8Z++bVT4G+Tw3GTxWqpXUCLOXcRmGLkPOzqtJ4osCoCmQBSzszGZTMClYgBs6bV2AzgPXs2TOTe1v40mBK8wOwhtn0kKcGwzNZaqldAEvbcgCwVDnxRIG5FGgFLHWrXrlZmqvClIMCAxQAsJ49q1a3NJjS/ACsAVY28KnBMQCr/pmG4AcLbfUBrGGdSCoUGEmBPGApZyZ9meUD8D4aDhS4EQUALABrFlMc8tTgxYAlTwb6n4N2TQWwZulzCkGBnAIlwFJvgGxG1biNbpZyBeCPAjegwGYAizNY17e2tqcC28KTFmS3COWnGXZGPVMFYCVS4oECcypQBCwj4zd48lfb6p+zwpSFAgMU2ARgxVt9Xa455D7AmuZOIpNuzz97Vu+QNeiauz2UhwIbUaAMWLUI9YH285+5qzdLG9GLZi5TAQArenpQ4AvAWqZBU2sUQIHbV6ALYN1+K6ghCpQVALAArLKFEIoCKIACIysAYI0sKNndpAIAFoB1k4ZJpVAABdarAIC13r6lZWcFACwA62wNuFAABVBgBgUArBlEpoirKwBgAVhXN0IqgAIosC0FAKxt9fdWWwtgAVhbtX3ajQIocCUFAKwrCU+xsyoAYA0ELDtB8EYDbAAbwAawAWwAG9BsAMAaCFizYjCFoQAKoMCKFLBfRrxQYO0KAFgA1tptnPahAArcmAIA1o11CNWZRAEAC8CaxLDIFAVQAAVyCgBYOWXwX5MCABaAtSZ7pi0ogAILUADAWkAnUcWLFQCwAKyLjYgMUAAFUKCPAgBWH7WIu1QFACwAa6m2S71RAAUWqgCAtdCOo9q9FACwAKxeBnNTkU8Hs9vtzOEU1OruaPa7XeVvwxrvRsQgDU4UQIFZFegCWHfH/ajjt09+xC2Yg8yxMp+66/3xLpuo1nNvClGyadsCTgc7z0+Td1vZbeEAFoDVZiOjhD/+8Wfzwsuv+7y+/eGJefWND431t6+2cJ8wdBQASxvs9UDcGS0szBY3CqDAtAq0AVbypSlf6ij693UAAAkZSURBVPujyX+N5+vcJz/i5nU05s4c9zuzC/vhyoCl1qnUhBnDACwAaxZz++XX38xLr7zjy3rz3ueV++0PHlWfbeE+YejoCVhVUpmo5e4rzA83CqDALAoUAUsb17ZWOf+2GufSaf6aX67sNcfNaVq1OVotujpgGWNcHW5tWgewAKzcUBrdXwDr9z/+Mp88+r7K//0HX/py2sJ9RHFoE1znwR5tLUqefKIACkyuQAmw6hWkgwl3/usKnczBbvv3/Bbtkx9xy11f6ROuXtnonefcCMzKRfUOVevWO5dxEwBYANa4FlXITQDKRrn30ddVzHfvf+FTtIX7iOIYCFjGDJuopVg+UQAFLlMgD1jKFpQvqhnmz0nFwOXmhRrEmml8VpUjDouvw9hxWHy9prhhW0J3PW8mRywuBKwaas/nZcP8u/Wxq6O2uhZW/wpuAAvAms3sQoC6/9nJvPbWx+ar7x778tvCfURxDAYsNznutLtkyZxPFECBqRTIA1b55ideYaqvw5WRGHz65EfcdNUwsIAcwAwGLKd3uCLWgOO67PY+dnV09Yh5O2jB7E4AC8Ca3eikwL+fPxen+tkWrp7J6DDYbWHxRK1WAE8UQIFJFMgClhu/uW3AZNxG8ZOn1aLwuDGN/IirbMueFau1VW5KRbf4qe3kOgTh/BycAJXk78gp6WNfxRrYwhUwH3QlB4AFYM1ien2fGoyfKlQrOXgFKz+41XLwRAEUGFWB0QCrOgJU/5zD4aSsQEVfznEjAKxakYYOsUjuuooTrjZJPKdxCWxSKFL6yuVXx22ekT375dP5pwlvaAkLwAKwZJhM+tn3qcH4qUK1coMBK95GUHPHEwVQYCIFsoDVcj5SBwHZ8rfneOIVltIXcnyjRdzSFuGogCXgm6xync9iNTmp1MdipC5OM6EEXuUTwAKwZjG8IU8Nhmey1EoOBSwZ3Dc0ENX24YkCK1WgFbC0lRLtN5gqfYIv3ySdg6bE3yZ06XwYcUu/MTYJYHWeg0t9LIMEwBIlzJMnTyZ9/5kBp67+Dx8+NHFc6/fgm5+qt28Ijk4KDHlqcCrASperOzWBSCiAAiMpkAeseFUpLFBfYfLj+Vj/s0O8VaWvetl80/yIG+rddNc6xyuEQ3+moQSzzXLtVVsf1ynqPOP+T3Obz4cVrAyIAVjjGuGQpwYnASy36nVLg3BcpckNBW5fgRJgqQ+v2CZVY7d5UFp+g0kOxdeAFMXRVrpz+RE3bzya/jb2oDNYAtJRX2mlRzsOah8H9ei8KKaVNbLf1QArXh26tWsAa2RLM8a0PRXYFp7USJsMs4P9vMR8SwMwaRMeKLABBYqAJVt34Xmq6Eu2lkjGdPAlLfH8tp+NKfGC1ReJl0wGxM2aX25uzfkHGfkVqHAPUvog7Gebxs7rvl+kP9r6OAPgQR2u4QSwWMG6ht2NU2YBsBp/8iwHKf2gHad4ckEBFBimQBmw6jzrlYrcoWdZAUn/W7T+Mk9/8b0tv7AlxA3VELeDnQa8Dl/Bcr1c/zq/zNH2M5inpR/iHQetj6u4cd2k6lf6BLAArCuZHsWiAApsVYEugLVVbW663bltwmtX2q2GBWx27RpV5QNYANZNGCKVQAEU2I4CANZS+9qtYt0Yydzi6pXtYQALwFrqSKfeKIACC1UAwFpox9lqy9mpG4GsehsxOKN1Q9JuArCePn1q2t7xIXsOud+QlVIVFECBVSkAYK2qO2lMRoHNANazZ89M7m3hC8DKWAjeKIACKDCyAgDWyIKS3U0qAGA9e1atbgFYN2mfVAoFUGCFCgBYK+xUmpQoAGANBCw7QfBGA2wAG8AGsAFsABvQbGAzgMUZrASu8UABFECBqyhgv4x4ocDaFdgEYMXbf12uOeS+dtOnfSiAAtdSAMC6lvKUO6cCABY/0zCnvVEWCqAAClTHK5ABBdauAIAFYK3dxmkfCqDAjSnACtaNdQjVmUQBAAvAmsSwyBQFUAAFcgoAWDll8F+TAgAWgLUme6YtKIACC1AAwFpAJ1HFixUAsACsi42oWwbuP6zCf03fHcypkFj+ST38d/Ukuvxtw25nSv/cMOjvFKo/No3yDcrbNdqyM7von9z9P76H8bRKBnnG/xqftNd6uHrtdqW/hyjprYXtTLY9rrxOdVMrjCcKNBXoAljJ+NHGTjPb4lWf/IhbS9lHh6L4Gw0EsACsyvQf//izeeHl1/0w+PaHJ+bVNz401t++2sJ9QsUhoJR+QZ/MIQsJNky+9AsgFsDJrgBsYwNW2pao4dq/zktdIxCT//ba7/cJpEW5VpeiZw6wJDytY05vAa6MzgCW1g34XaBAG2Al4zU3djrWoU9+xK1F7aNDx27YXDQAC8CqjP6XX38zL73yjh8Ab977vHK//cGjTuE+YeRIBmkUnr10X+qHw6FaWUlhwaV0E+/+cDB7C2SZu9xB9ZA6hMtsUt7xLlv1KuDuzmgx5I6wUU3J07WhERaXUsXdm8NhbzTAGtROA2DFMnM9rQJFwNLGna1Ozr+tqrl0mr/mlyubuG3Kbz4cwAKw/CAQwPr9j7/MJ4++r/zff/Bl53AfURxuAsrCkcRLPoMvfAcf8fabTyJwcrwzJbgohfm8Yoc2gQblxdE7XWvpA7+qnvEKV5BxFX44mRrUoi3CMfQOyvLOwfn6HHCgQEOBEmDVY1VbTXWr2sU7kEYx1UWf/Ihb69dHh1RxfEQBAAvAEltorGDd++jryv/d+190DvcRnSM/SOOY0bUDjno1SmArgglJEsCJMW4CVgClrksmD8kr/pwCsDRYCdtQhWfq6eLZ7xcNsAbrzQpW3PNcT6xAHrDceFfGsBE7dWGyGpysWrsx1pg/OuQX59+UIK5XfB3GjsPi6zXFDduCO1YAwFoYYH366afmxRdfNP/880+jL+31e++9Z+wv0A99yQqWTX//s5N57a2PzVffPfbZtYX7iJUjDzvNeOmVTJxyoyrX6kpYCCc2Kw1gKm97nisDLmkVah+Xl9Sj8ozLy6XN+NcQpB+cr9vnJuNGoXVmtQ71nX3tDtszXG//xZI7w5bRNNNEvFGgVYE8YJVXqeKbiPTGKYaZPvkRtz4N0UeH1q7edIT/B2M4uwmeBM4DAAAAAElFTkSuQmCC[/img] 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[/img]
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GD32FFPRTGU6 DSP指令集和专用浮点运算单元(FPU) 的使用方法 sos
[i=s] 本帖最后由 yupanshanhou 于 2021-5-14 14:40 编辑 [/i] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]全新GD32FFPR系列指纹专用MCU采用168MHz高性能ARM[/size][/backcolor]®[backcolor=rgb(255, 255, 255)][size=16px] Cortex[/size][/backcolor]®[backcolor=rgb(255, 255, 255)][size=16px]-M4内核,[/size][/backcolor][/color][/font][font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]提供了完整的DSP指令集和专用浮点运算单元(FPU),可直接支持三角函数、滤波和卷积等复杂运算以加快指纹算法执行速度。[/size][/backcolor][/color][/font] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]内核访问闪存高速零等待,最高主频下的工作性能可达210DMIPS,CoreMark[/size][/backcolor]®[backcolor=rgb(255, 255, 255)][size=16px]测试可达565分。[/size][/backcolor][/color][/font] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]从而以增强的动力支持高级指纹识别运算的全过程,[/size][/backcolor][/color][/font] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]包括指纹图像预处理、分割拼接、数据特征提取、特征匹配、交叉比对、识别解锁等一系列指令,[/size][/backcolor][/color][/font] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]更可显著提高指纹注册和匹配效率。[/size][/backcolor][/color][/font] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]芯片更配备了1MB的大容量内置Flash和多达128KB的SRAM,[/size][/backcolor][/color][/font] [font=微软雅黑][color=#8b0000][backcolor=rgb(255, 255, 255)][size=16px]可支持多枚指纹信息存储和动态分配内存等识别过程的资源开销。[/size][/backcolor][/color][/font] [color=#0000ff][font=宋体][backcolor=rgb(255, 255, 255)][size=16px](以上文字来源: [/size][/backcolor][/font][font=宋体][backcolor=rgb(255, 255, 255)][size=16px]https://www.21ic.com/np/mcu/201707/727572.htm[/size][/backcolor][/font][font=宋体][backcolor=rgb(255, 255, 255)][size=16px])[/size][/backcolor][/font][/color] [color=#333333][font=宋体][backcolor=rgb(255, 255, 255)][size=7]请问哪位知道[/size][/backcolor][/font][/color] [color=#333333][font=宋体][size=5][backcolor=rgb(255, 255, 255)][b]1.这款芯片的DSP指令集是否就是ARM官方提供的DSP指令集?[/b][/backcolor][/size][/font][/color] [b][size=5][color=#333333][font=宋体][backcolor=rgb(255, 255, 255)]2.专用的FPU有什么特殊用法吗?[/backcolor][/font][/color] [color=#333333][font=宋体][backcolor=rgb(255, 255, 255)]3.这款芯片与STM32M4系列的单片机相比,使用DSP库的方法是否完全一样?[/backcolor][/font][/color][/size][/b]
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2.4G发射接收低噪声问题
目前有做1他2.4G 装置,发射板咪头拾取声音到DSP通过2.4G模块发射出去, 然后接收板这边通过2.4G模块接收给到DSP再输出给运放最后到喇叭。 现在发现单上电接收板,喇叭基本上没有噪声。 一接上发射板(注意这时候我没有接咪头,所以不会拾取外界声音信号),喇叭就有比较大的低噪声。这说明低噪声来自发射板,那么这个问题要怎么解决?方向在哪里?是发射板布线还是2.4G高频信号的影响?
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想请问大家有做过DSP多机重联,错相PWM输出的吗? sos
想用DSP做多模块Buck变换器的并联错相控制,但是多台控制器DSP之间的时间要如何实现同步呢?想问一下有没有做过的朋友?
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