/** ****************************************************************************** * @file system_stm32f7xx.c ****************************************************************************** * @attention * *

© COPYRIGHT(c) 2017 STMicroelectronics

* * Redistribution and use in source and binary forms, with or without modification, * are permitted provided that the following conditions are met: * 1. Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * 3. Neither the name of STMicroelectronics nor the names of its contributors * may be used to endorse or promote products derived from this software * without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * ****************************************************************************** */ /* ---------------------------------------------------------------------- * Project: TinyEngine * Title: system_stm32f7xx.c * * Reference papers: * - MCUNet: Tiny Deep Learning on IoT Device, NeurIPS 2020 * - MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning, NeurIPS 2021 * - MCUNetV3: On-Device Training Under 256KB Memory, NeurIPS 2022 * Contact authors: * - Wei-Ming Chen, wmchen@mit.edu * - Wei-Chen Wang, wweichen@mit.edu * - Ji Lin, jilin@mit.edu * - Ligeng Zhu, ligeng@mit.edu * - Song Han, songhan@mit.edu * * Target ISA: ARMv7E-M * -------------------------------------------------------------------- */ #include "stm32f7xx.h" #if !defined(HSE_VALUE) #define HSE_VALUE ((uint32_t)25000000) #endif #if !defined(HSI_VALUE) #define HSI_VALUE ((uint32_t)16000000) #endif #define VECT_TAB_OFFSET 0x00 uint32_t SystemCoreClock = 16000000; const uint8_t AHBPrescTable[16] = {0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 6, 7, 8, 9}; const uint8_t APBPrescTable[8] = {0, 0, 0, 0, 1, 2, 3, 4}; void SystemInit(void) { #if (__FPU_PRESENT == 1) && (__FPU_USED == 1) SCB->CPACR |= ((3UL << 10 * 2) | (3UL << 11 * 2)); #endif RCC->CR |= (uint32_t)0x00000001; RCC->CFGR = 0x00000000; RCC->CR &= (uint32_t)0xFEF6FFFF; RCC->PLLCFGR = 0x24003010; RCC->CR &= (uint32_t)0xFFFBFFFF; RCC->CIR = 0x00000000; #ifdef VECT_TAB_SRAM SCB->VTOR = RAMDTCM_BASE | VECT_TAB_OFFSET; #else SCB->VTOR = FLASH_BASE | VECT_TAB_OFFSET; #endif } void SystemCoreClockUpdate(void) { uint32_t tmp = 0, pllvco = 0, pllp = 2, pllsource = 0, pllm = 2; tmp = RCC->CFGR & RCC_CFGR_SWS; switch (tmp) { case 0x00: SystemCoreClock = HSI_VALUE; break; case 0x04: SystemCoreClock = HSE_VALUE; break; case 0x08: pllsource = (RCC->PLLCFGR & RCC_PLLCFGR_PLLSRC) >> 22; pllm = RCC->PLLCFGR & RCC_PLLCFGR_PLLM; if (pllsource != 0) { pllvco = (HSE_VALUE / pllm) * ((RCC->PLLCFGR & RCC_PLLCFGR_PLLN) >> 6); } else { pllvco = (HSI_VALUE / pllm) * ((RCC->PLLCFGR & RCC_PLLCFGR_PLLN) >> 6); } pllp = (((RCC->PLLCFGR & RCC_PLLCFGR_PLLP) >> 16) + 1) * 2; SystemCoreClock = pllvco / pllp; break; default: SystemCoreClock = HSI_VALUE; break; } tmp = AHBPrescTable[((RCC->CFGR & RCC_CFGR_HPRE) >> 4)]; SystemCoreClock >>= tmp; }