DDR2 SDRAM设备操作时序图解

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"三星公司提供的DDR2标准命令与操作时序描述文件,涵盖了DDR2 SDRAM设备的操作状态、时序图以及简化的状态转换图,旨在详细解释DDR2内存的工作流程。" DDR2 SDRAM(Double Data Rate Second Generation Synchronous Dynamic Random Access Memory)是一种高速同步动态随机存取存储器,其设计目标是提高数据传输速率和系统性能。DDR2通过在每个时钟周期的上升沿和下降沿都传输数据,相比于DDR1实现了数据传输速率的翻倍。 **设备操作与时序图** DDR2 SDRAM的操作主要包括以下几个步骤: 1. **自我空闲(Self Idle)**: 设备处于非活动状态,等待命令输入。 2. **设置(Setting)**: 包括OCD(On-Chip Driver)校准,这是为了确保数据线驱动器的输出电平适应系统的需求。 3. **EMRS(Extended Mode Register Set)**: 用于设置模式寄存器,这些寄存器包含了关于内存模块配置的参数,如CAS延迟等。 4. **银行预充(Bank Precharging)**: 所有银行关闭,准备新的操作。 5. **激活(Activate, ACT)**: 选择一个银行并设置行地址,允许读写操作。 6. **RDA(Read Address)**: 读取地址,启动读取操作。 7. **读取(Read)**: 从选定的行地址获取数据。 8. **CKEL(CK Low, enter Power Down)**: 降低CKE(Clock Enable)信号,进入低功耗状态。 9. **(E)MRS**: 可能是基本的MRS或扩展的MRS,用于更新或初始化模式寄存器。 10. **CKEH(CK High, exit Power Down, exit Self Refresh)**: 提升CKE信号,退出低功耗或自刷新状态。 11. **写入(Write, WRA)**: 写入地址,启动写入操作,可能带有自动预充功能。 12. **PR(Precharge)**: 预充所有银行,准备下一轮操作。 13. **RFA(Read with Autoprecharge)**: 自动预充读取,读取后自动预充银行。 14. **WRA(Write with Autoprecharge)**: 自动预充写入,写入后自动预充银行。 15. **刷新(REF)**: 刷新操作,确保数据不会丢失,DDR2 SDRAM通常每3.9us刷新一次。 16. **SRF(Enter Self Refresh)**: 进入自刷新模式,保持数据的同时降低功耗。 **简化状态转换图** 该图展示了DDR2 SDRAM从所有银行预充状态开始,如何通过不同的命令转换到激活、读取、写入、预充和自刷新状态。注意,这个图不是详尽无遗的,它主要描绘了可能的状态转换路径,而不是每一个可能的细节。例如,在涉及多银行操作时,情况可能会更复杂。 在DDR2 SDRAM中,命令序列的正确执行至关重要,例如,激活操作后必须遵循预充或读/写操作,而不能直接回到自我空闲状态。此外,CKE信号的高低控制着设备的活动状态,当CKE为低时,设备进入低功耗或自刷新状态,CKE为高则恢复活动。 DDR2 SDRAM的操作涉及到复杂的时序和状态转换,通过精确的命令控制实现高效的数据读写和电源管理。理解和掌握这些时序有助于优化系统设计,提高系统的稳定性和性能。

clear;clc parentdir = 'F:\data process\fMRI\fmrioutput'; % 定义储存各被试源文件的上级文件夹 cd(parentdir); % 进入这个上级文件夹 allsubjects = dir('sub*');%查找该文件夹下的所有被试 subinfos = numel(allsubjects); for i=1:numel(allsubjects) % 对每个被试进行循环 cursubject = allsubjects(i).name; % 找到当前被试的名字 matlabbatch=cell(1); curWPAT = fullfile(parentdir,cursubject,'WPAT'); curfucout=fullfile('F:\data process\fMRI\fmrioutput',cursubject,'WPAT') matlabbatch{1}.spm.stats.fmri_spec.dir = {curfucout}; matlabbatch{1}.spm.stats.fmri_spec.sess.scans = cellstr(spm_select('ExtFPList', curWPAT, '^sw*.nii', Inf)) matlabbatch{1}.spm.stats.fmri_spec.timing.units = 'scans'; matlabbatch{1}.spm.stats.fmri_spec.timing.RT = 2; matlabbatch{1}.spm.stats.fmri_spec.timing.fmri_t = 16; matlabbatch{1}.spm.stats.fmri_spec.timing.fmri_t0 = 8; %% matlabbatch{1}.spm.stats.fmri_spec.sess.cond = struct('name', {}, 'onset', {}, 'duration', {}, 'tmod', {}, 'pmod', {}, 'orth', {}); matlabbatch{1}.spm.stats.fmri_spec.sess.multi = {'D:\data process\fMRI\onsets\subject(i)_run1.mat'}; matlabbatch{1}.spm.stats.fmri_spec.sess.regress = struct('name', {}, 'val', {}); matlabbatch{1}.spm.stats.fmri_spec.sess.tempxx=dir(fullfile(curfucout,'rp*.txt')) matlabbatch{1}.spm.stats.fmri_spec.sess.hpf = 128; matlabbatch{1}.spm.stats.fmri_spec.fact = struct('name', {}, 'levels', {}); matlabbatch{1}.spm.stats.fmri_spec.bases.hrf.derivs = [0 0]; matlabbatch{1}.spm.stats.fmri_spec.volt = 1; matlabbatch{1}.spm.stats.fmri_spec.global = 'None'; matlabbatch{1}.spm.stats.fmri_spec.mthresh = 0.8; matlabbatch{1}.spm.stats.fmri_spec.mask = {''}; matlabbatch{1}.spm.stats.fmri_spec.cvi = 'AR(1)'; matlabbatch{2}.spm.stats.fmri_est.spmmat(1) = cfg_dep('fMRI model specification: SPM.mat File', substruct('.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','spmmat')); matlabbatch{2}.spm.stats.fmri_est.write_residuals = 0; matlabbatch{2}.spm.stats.fmri_est.method.Classical = 1; matlabbatch{3}.spm.stats.con.spmmat(1) = cfg_dep('Model estimation: SPM.mat File', substruct('.','val', '{}',{2}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','spmmat')); matlabbatch{3}.spm.stats.con.consess{1}.tcon.name = 'Old'; matlabbatch{3}.spm.stats.con.consess{1}.tcon.weights = 1; matlabbatch{3}.spm.stats.con.consess{1}.tcon.sessrep = 'none'; matlabbatch{3}.spm.stats.con.delete = 0; end;怎么改

2023-05-24 上传

torch.save(model.state_dict(), r'./saved_model/' + str(args.arch) + '_' + str(args.batch_size) + '_' + str(args.dataset) + '_' + str(args.epoch) + '.pth') # 计算GFLOPs flops = 0 for name, module in model.named_modules(): if isinstance(module, torch.nn.Conv2d): flops += module.weight.numel() * 2 * module.in_channels * module.out_channels * module.kernel_size[ 0] * module.kernel_size[1] / module.stride[0] / module.stride[1] elif isinstance(module, torch.nn.Linear): flops += module.weight.numel() * 2 * module.in_features start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() with torch.no_grad(): output = UNet(args,3,1).to(device) end_event.record() torch.cuda.synchronize() elapsed_time_ms = start_event.elapsed_time(end_event) gflops = flops / (elapsed_time_ms * 10 ** 6) print("GFLOPs: {:.2f}".format(gflops)) return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops出现错误 best_iou,aver_iou,aver_dice,aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, FPS, parameters, gflops = val(model,best_iou,val_dataloader) File "D:/BaiduNetdiskDownload/0605_ghostv2unet _tunnelcrack/ghostunet++/UNET++/main.py", line 143, in val return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops UnboundLocalError: local variable 'gflops' referenced before assignment怎么修改

2023-06-08 上传

#!/usr/bin/env python #coding: utf-8 import os from time import time from datetime import datetime from netmiko import ConnectHandler from openpyxl import Workbook from openpyxl import load_workbook def read_device_excel( ): ip_list = [] wb1 = load_workbook('E:\/Users/Wayne_Peng/Desktop/cs_lab.xlsx') ws1 = wb1.get_sheet_by_name("Sheet1") for cow_num in range(2,ws1.max_row+1): ipaddr = ws1["a"+str(cow_num)].value ip_list.append(ipaddr) return ip_list def get_config(ipaddr): session = ConnectHandler(device_type="huawei", ip=ipaddr, username="mtlops", password="cisco,123", banner_timeout=300) print("connecting to "+ ipaddr) print ("---- Getting HUAWEI configuration from {}-----------".format(ipaddr)) # config_data = session.send_command('screen-length 0 temporary') # config_data = session.send_command('dis cu | no-more ') # command = 'display version | display cpu-usage | display memory-usage' # config_data = session.send_command(command) commands = ['display version', 'display cpu-usage', 'display memory-usage'] config_data = '' for cmd in commands: output = session.send_command_timing(cmd) config_data += f'{cmd}\n{output}\n' session.disconnect() return config_data def write_config_to_file(config_data,ipaddr): now = datetime.now() date= "%s-%s-%s"%(now.year,now.month,now.day) time_now = "%s-%s"%(now.hour,now.minute) #---- Write out configuration information to file config_path = 'E:\/Users/Wayne_Peng/Desktop/' +date verify_path = os.path.exists(config_path) if not verify_path: os.makedirs(config_path) config_filename = config_path+"/"+'config_' + ipaddr +"_"+date+"_" + time_now # Important - create unique configuration file name print ('---- Writing configuration: ', config_filename) with open( config_filename, "w",encoding='utf-8' ) as config_out: config_out.write( config_data ) return def main(): starting_time = time() ip_list = read_device_excel() for ipaddr in ip_list: hwconfig = get_config(ipaddr) write_config_to_file(hwconfig,ipaddr) print ('\n---- End get config threading, elapsed time=', time() - starting_time) #======================================== # Get config of HUAWEI #======================================== if __name__ == '__main__': main() 加一段gevent,def run_gevent()

2023-05-26 上传

代码优化: quarter_dict = { '1': [date(year=timing.year, month=1, day=1), date(year=timing.year, month=3, day=calendar.monthrange(timing.year, 3)[-1])], '2': [date(year=timing.year, month=1, day=1), date(year=timing.year, month=3, day=calendar.monthrange(timing.year, 3)[-1])], '3': [date(year=timing.year, month=1, day=1), date(year=timing.year, month=3, day=calendar.monthrange(timing.year, 3)[-1])], '4': [date(year=timing.year, month=4, day=1), date(year=timing.year, month=6, day=calendar.monthrange(timing.year, 6)[-1])], '5': [date(year=timing.year, month=4, day=1), date(year=timing.year, month=6, day=calendar.monthrange(timing.year, 6)[-1])], '6': [date(year=timing.year, month=4, day=1), date(year=timing.year, month=6, day=calendar.monthrange(timing.year, 6)[-1])], '7': [date(year=timing.year, month=7, day=1), date(year=timing.year, month=9, day=calendar.monthrange(timing.year, 9)[-1])], '8': [date(year=timing.year, month=7, day=1), date(year=timing.year, month=9, day=calendar.monthrange(timing.year, 9)[-1])], '9': [date(year=timing.year, month=7, day=1), date(year=timing.year, month=9, day=calendar.monthrange(timing.year, 9)[-1])], '10': [date(year=timing.year, month=10, day=1), date(year=timing.year, month=12, day=calendar.monthrange(timing.year, 12)[-1])], '11': [date(year=timing.year, month=10, day=1), date(year=timing.year, month=12, day=calendar.monthrange(timing.year, 12)[-1])], '12': [date(year=timing.year, month=10, day=1), date(year=timing.year, month=12, day=calendar.monthrange(timing.year, 12)[-1])] }

2023-05-27 上传

import csv import glob import os path = "D:\cclog\cclog" class StartUpTimeAnalysis: def init(self,fn): ext = os.path.splitext(fn)[-1].lower() if ext == '.xml': # self.root = etree.parse(fn) self.prepare_xml() else: with open(fn,'r') as fin: self.text = fin.read() # for line in fin: # if '[START UP TIMING]' in line: # # self.text += '\n%s' % line # self.text += line self.prepare_log() def prepare_xml(self): data = {} _app_init_done_delay = self.app_init_done_delay.split(" ")[-4] _graph_init_done_delay = self.graph_init_done_delay.split(" ")[-4] _render_frame_done_delay = self.render_frame_done_delay.split(" ")[-5] data["_app_init_done_delay"] = _app_init_done_delay data["_graph_init_done_delay"] = _graph_init_done_delay data["_render_frame_done_delay"] = _render_frame_done_delay return data def prepare_log(self): raw = self.text self.app_init_done_delay = '\n'.join( [el for el in raw.split('\n') if 'after appInit @' in el]) self.graph_init_done_delay = '\n'.join( [el for el in raw.split('\n') if 'avm graph init done' in el]) self.render_frame_done_delay = '\n'.join([el for el in raw.split('\n') if 'cc_render_renderFrame num:0' in el]) if name == 'main': line = ['index','LOG_FILE_NAME', 'APP_INIT_DONE_DELAY', 'GRAPH_INIT_DONE_DELAY', 'RENDER_FRAME_DONE_DELAY'] resultFilePath = os.path.join(path, "result_cold_start_time.csv") fout = open(resultFilePath, 'w', newline='') book = csv.writer(fout) book.writerow(line) print(os.path.join(path + '/**/VisualApp.localhost.root.log.ERROR*')) app_init_done_delay = [] graph_init_done_delay = [] render_frame_done_delay = [] for file_name in glob.glob(os.path.join(path + '/**/VisualApp.localhost.root.log.ERROR*')): res = {} index = os.path.dirname(file_name).split("\\")[-1] res['INDEX'] = index res['LOG_FILE_NAME'] = "VisualApp.localhost.root.log.ERROR_" + index st = StartUpTimeAnalysis(file_name) data = st.prepare_xml() res.update(data) app_init_done_delay.append(float(res["_app_init_done_delay"])) graph_init_done_delay.append(float(res["_graph_init_done_delay"])) render_frame_done_delay.append(float(res["_render_frame_done_delay"])) values = res.values() book.writerow(values) DA_MAX = ['', "MAX_VALUE", max(app_init_done_delay), max(graph_init_done_delay), max(render_frame_done_delay)] DA_MIN = ['', "MIN_VALUE", min(app_init_done_delay), min(graph_init_done_delay), min(render_frame_done_delay)] DA_AVG = ['', "AVG_VALUE", sum(app_init_done_delay)/len(app_init_done_delay), sum(graph_init_done_delay)/len(graph_init_done_delay), sum(render_frame_done_delay)/len(render_frame_done_delay)] book.writerow(DA_MAX) book.writerow(DA_MIN) book.writerow(DA_AVG) fout.close() 解释一下每行代码的意思

2023-07-15 上传