/**
******************************************************************************
* @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
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*
******************************************************************************
*/
/* ----------------------------------------------------------------------
* 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;
}