В этом примере показано, как преобразовать модель регрессии нейронной сети в Simulink к фиксированной точке с помощью fxpopt функция и Оптимизатор Интерполяционной таблицы.
Fixed-Point Designer обеспечивает рабочие процессы через Инструмент Фиксированной точки, который может преобразовать проект от типов данных с плавающей точкой до типов данных с фиксированной точкой. fxpopt функция оптимизирует типы данных в основанном на модели в заданной системе поведенческие ограничения. Для получения дополнительной информации отошлите к ссылке на документацию https://www.mathworks.com/help/fixedpoint/ref/fxpopt.html, Оптимизатор Интерполяционной таблицы генерирует эффективные памятью замены интерполяционной таблицы для неограниченных функций, таких как exp и log2. Используя эти инструменты, этот пример демонстрирует, как преобразовать обученную модель регрессии нейронной сети с плавающей точкой, чтобы использовать встроено-эффективные типы данных с фиксированной точкой.
engine_dataset содержит данные, представляющие отношение между топливным уровнем и скоростью механизма, и его крутящим моментом и выбросами газа.
% Use the function fitting tool (nftool) from Deep Learning Toolbox (TM) to % train a neural network to estimate torque and gas emissions of an engine % given the fuel rate and speed. Use the following commands to train % the neural network. load engine_dataset; x = engineInputs; t = engineTargets; net = fitnet(10); net = train(net,x,t); view(net)
Закройте все окна учебного инструмента и представление сети.
nnet.guis.closeAllViews();
nntraintool('close');
Если сеть обучена, используйте gensim функция от Deep Learning Toolbox(TM), чтобы сгенерировать модель Simulink.
[sysName, netName] = gensim(net, 'Name', 'mTrainedNN');
Модель сгенерирована gensim функция содержит нейронную сеть с обученными весами и смещениями. Чтобы подготовить эту сгенерированную модель к преобразованию фиксированной точки, выполните шаги подготовки в инструкциях по лучшым практикам. https://www.mathworks.com/help/fixedpoint/ug/best-practices-for-using-the-fixed-point-tool-to-propose-data-types-for-your-simulink-model.html
После применения этих принципов обученная нейронная сеть далее изменяется, чтобы включить логгирование сигнала при выходе сети, добавить входные стимулы и блоки верификации.
Откройте и смотрите модель.
model = 'ex_fxpdemo_neuralnet_regression'; system_under_design = [model '/Function Fitting Neural Network']; baseline_output = [model '/yarr']; open_system(model); % Set up model for HDL code generation hdlsetup(model);
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','SingleTaskRateTransMsg')">SingleTaskRateTransMsg</a> value is set from 'none' to 'error' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','SingleTaskRateTransMsg', 'none')">revert</a>).
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','Solver')">Solver</a> value is set from 'FixedStepAuto' to 'FixedStepDiscrete' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','Solver', 'FixedStepAuto')">revert</a>).
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','AlgebraicLoopMsg')">AlgebraicLoopMsg</a> value is set from 'warning' to 'error' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','AlgebraicLoopMsg', 'warning')">revert</a>).
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','BlockReduction')">BlockReduction</a> value is set from 'on' to 'off' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','BlockReduction', 'on')">revert</a>).
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','ConditionallyExecuteInputs')">ConditionallyExecuteInputs</a> value is set from 'on' to 'off' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','ConditionallyExecuteInputs', 'on')">revert</a>).
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','DefaultParameterBehavior')">DefaultParameterBehavior</a> value is set from 'Tunable' to 'Inlined' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','DefaultParameterBehavior', 'Tunable')">revert</a>).
### <a href="matlab:configset.internal.open('ex_fxpdemo_neuralnet_regression','ProdHWDeviceType')">ProdHWDeviceType</a> value is set from 'Intel->x86-64 (Windows64)' to 'ASIC/FPGA->ASIC/FPGA' (<a href="matlab:set_param('ex_fxpdemo_neuralnet_regression','ProdHWDeviceType', 'Intel->x86-64 (Windows64)')">revert</a>).
### The listed configuration parameter values are modified as a part of hdlsetup. Please refer to <a href="matlab:helpview(fullfile(docroot, 'hdlcoder', 'helptargets.map'), 'msg_hdlsetup_function')">hdlsetup</a> document for best practices on model settings.
Симулируйте модель, чтобы наблюдать производительность модели при использовании типов данных с плавающей точкой с двойной точностью.
loggingInfo = get_param(model, 'DataLoggingOverride'); sim_out = sim(model, 'SaveFormat', 'Dataset'); plotRegression(sim_out, baseline_output, system_under_design, 'Regression before conversion');
opts = fxpOptimizationOptions(); opts.addTolerance(system_under_design, 1, 'RelTol', 0.05); opts.addTolerance(system_under_design, 1, 'AbsTol', 50) opts.AllowableWordLengths = 8:32;
Используйте fxpopt функция, чтобы оптимизировать типы данных в системе в соответствии с проектом и исследовать решение. Программное обеспечение анализирует область значений объектов в system_under_design и wordlength и ограничения допуска заданы в opts применять типы гетерогенных данных к модели при минимизации общей битной ширины.
solution = fxpopt(model, system_under_design, opts); best_solution = solution.explore;
+ Checking for unsupported constructs.
- The paths below have constructs that do not support fixed-point data types. These constructs will be surrounded with Data Type Conversion blocks.
'ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1/tansig/tanh'
+ Preprocessing
+ Modeling the optimization problem
- Constructing decision variables
+ Running the optimization solver
- Evaluating new solution: cost 515, does not meet the tolerances.
- Evaluating new solution: cost 577, does not meet the tolerances.
- Evaluating new solution: cost 639, does not meet the tolerances.
- Evaluating new solution: cost 701, does not meet the tolerances.
- Evaluating new solution: cost 763, does not meet the tolerances.
- Evaluating new solution: cost 825, does not meet the tolerances.
- Evaluating new solution: cost 887, does not meet the tolerances.
- Evaluating new solution: cost 949, meets the tolerances.
- Updated best found solution, cost: 949
- Evaluating new solution: cost 945, meets the tolerances.
- Updated best found solution, cost: 945
- Evaluating new solution: cost 944, meets the tolerances.
- Updated best found solution, cost: 944
- Evaluating new solution: cost 943, meets the tolerances.
- Updated best found solution, cost: 943
- Evaluating new solution: cost 942, meets the tolerances.
- Updated best found solution, cost: 942
- Evaluating new solution: cost 941, meets the tolerances.
- Updated best found solution, cost: 941
- Evaluating new solution: cost 940, meets the tolerances.
- Updated best found solution, cost: 940
- Evaluating new solution: cost 939, meets the tolerances.
- Updated best found solution, cost: 939
- Evaluating new solution: cost 938, meets the tolerances.
- Updated best found solution, cost: 938
- Evaluating new solution: cost 937, meets the tolerances.
- Updated best found solution, cost: 937
- Evaluating new solution: cost 936, meets the tolerances.
- Updated best found solution, cost: 936
- Evaluating new solution: cost 926, meets the tolerances.
- Updated best found solution, cost: 926
- Evaluating new solution: cost 925, meets the tolerances.
- Updated best found solution, cost: 925
- Evaluating new solution: cost 924, meets the tolerances.
- Updated best found solution, cost: 924
- Evaluating new solution: cost 923, meets the tolerances.
- Updated best found solution, cost: 923
- Evaluating new solution: cost 922, meets the tolerances.
- Updated best found solution, cost: 922
- Evaluating new solution: cost 917, meets the tolerances.
- Updated best found solution, cost: 917
- Evaluating new solution: cost 916, meets the tolerances.
- Updated best found solution, cost: 916
- Evaluating new solution: cost 914, meets the tolerances.
- Updated best found solution, cost: 914
- Evaluating new solution: cost 909, meets the tolerances.
- Updated best found solution, cost: 909
- Evaluating new solution: cost 908, meets the tolerances.
- Updated best found solution, cost: 908
- Evaluating new solution: cost 906, meets the tolerances.
- Updated best found solution, cost: 906
- Evaluating new solution: cost 898, meets the tolerances.
- Updated best found solution, cost: 898
- Evaluating new solution: cost 897, meets the tolerances.
- Updated best found solution, cost: 897
- Evaluating new solution: cost 893, does not meet the tolerances.
- Evaluating new solution: cost 896, meets the tolerances.
- Updated best found solution, cost: 896
- Evaluating new solution: cost 895, meets the tolerances.
- Updated best found solution, cost: 895
- Evaluating new solution: cost 894, meets the tolerances.
- Updated best found solution, cost: 894
- Evaluating new solution: cost 893, meets the tolerances.
- Updated best found solution, cost: 893
- Evaluating new solution: cost 892, meets the tolerances.
- Updated best found solution, cost: 892
- Evaluating new solution: cost 891, meets the tolerances.
- Updated best found solution, cost: 891
- Evaluating new solution: cost 890, meets the tolerances.
- Updated best found solution, cost: 890
- Evaluating new solution: cost 889, meets the tolerances.
- Updated best found solution, cost: 889
- Evaluating new solution: cost 888, meets the tolerances.
- Updated best found solution, cost: 888
- Evaluating new solution: cost 878, meets the tolerances.
- Updated best found solution, cost: 878
- Evaluating new solution: cost 877, meets the tolerances.
- Updated best found solution, cost: 877
- Evaluating new solution: cost 876, meets the tolerances.
- Updated best found solution, cost: 876
- Evaluating new solution: cost 875, meets the tolerances.
- Updated best found solution, cost: 875
- Evaluating new solution: cost 874, meets the tolerances.
- Updated best found solution, cost: 874
- Evaluating new solution: cost 869, meets the tolerances.
- Updated best found solution, cost: 869
- Evaluating new solution: cost 868, does not meet the tolerances.
- Evaluating new solution: cost 867, meets the tolerances.
- Updated best found solution, cost: 867
- Evaluating new solution: cost 862, does not meet the tolerances.
- Evaluating new solution: cost 866, does not meet the tolerances.
- Evaluating new solution: cost 865, does not meet the tolerances.
- Evaluating new solution: cost 859, meets the tolerances.
- Updated best found solution, cost: 859
+ Optimization has finished.
- Neighborhood search complete.
- Maximum number of iterations completed.
+ Fixed-point implementation that met the tolerances found.
- Total cost: 859
- Maximum absolute difference: 49.714162
- Use the explore method of the result to explore the implementation.
Проверьте точность модели после преобразования путем симуляции модели.
set_param(model, 'DataLoggingOverride', loggingInfo); Simulink.sdi.markSignalForStreaming([model '/yarr'], 1, 'on'); Simulink.sdi.markSignalForStreaming([model '/diff'], 1, 'on'); sim_out = sim(model, 'SaveFormat', 'Dataset');
Постройте точность регрессии модели фиксированной точки.
plotRegression(sim_out, baseline_output, system_under_design, 'Regression after conversion');
Функция Активации Tanh в Слое 1 может быть заменена или интерполяционной таблицей или реализацией CORDIC для более эффективной генерации фиксированной точки. В этом примере мы будем использовать Оптимизатор Интерполяционной таблицы, чтобы получить интерполяционную таблицу как замену для tanh. Мы будем использовать EvenPow2Spacing для более быстрой скорости выполнения. Для получения дополнительной информации см. https://www.mathworks.com/help/fixedpoint/ref/functionapproximation.options-class.html.
block_path = [system_under_design '/Layer 1/tansig']; p = FunctionApproximation.Problem(block_path); p.Options.WordLengths = 8:32; p.Options.BreakpointSpecification = 'EvenPow2Spacing'; solution = p.solve; solution.replaceWithApproximate;
| ID | Memory (bits) | Feasible | Table Size | Breakpoints WLs | TableData WL | BreakpointSpecification | Error(Max,Current) | | 0 | 44 | 0 | 2 | 14 | 8 | EvenPow2Spacing | 7.812500e-03, 1.000000e+00 | | 1 | 8220 | 1 | 1024 | 14 | 8 | EvenPow2Spacing | 7.812500e-03, 7.812500e-03 | | 2 | 8212 | 1 | 1024 | 10 | 8 | EvenPow2Spacing | 7.812500e-03, 7.812500e-03 | | 3 | 4124 | 1 | 512 | 14 | 8 | EvenPow2Spacing | 7.812500e-03, 7.812500e-03 | | 4 | 4114 | 1 | 512 | 9 | 8 | EvenPow2Spacing | 7.812500e-03, 7.812500e-03 | | 5 | 46 | 0 | 2 | 14 | 9 | EvenPow2Spacing | 7.812500e-03, 1.000000e+00 | | 6 | 48 | 0 | 2 | 14 | 10 | EvenPow2Spacing | 7.812500e-03, 1.000000e+00 | | 7 | 50 | 0 | 2 | 14 | 11 | EvenPow2Spacing | 7.812500e-03, 1.000000e+00 | | 8 | 52 | 0 | 2 | 14 | 12 | EvenPow2Spacing | 7.812500e-03, 1.000000e+00 | | 9 | 54 | 0 | 2 | 14 | 13 | EvenPow2Spacing | 7.812500e-03, 1.000000e+00 | Best Solution | ID | Memory (bits) | Feasible | Table Size | Breakpoints WLs | TableData WL | BreakpointSpecification | Error(Max,Current) | | 4 | 4114 | 1 | 512 | 9 | 8 | EvenPow2Spacing | 7.812500e-03, 7.812500e-03 |
Проверьте точность модели после функциональной замены
sim_out = sim(model, 'SaveFormat', 'Dataset');
Постройте точность регрессии после функциональной замены.
plotRegression(sim_out, baseline_output, system_under_design, 'Regression after function replacement');
Генерация HDL-кода требует лицензии HDL Coder™.
Выберите модель, для которой можно сгенерировать HDL-код и испытательный стенд.
systemname = 'ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network';
Используйте временную директорию для сгенерированных файлов.
workingdir = tempname;
Можно запустить следующую команду, чтобы проверять на совместимость генерации HDL-кода.
checkhdl(systemname,'TargetDirectory',workingdir);
### Starting HDL check. ### Creating HDL Code Generation Check Report file://C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Function_Fitting_Neural_Network_report.html ### HDL check for 'ex_fxpdemo_neuralnet_regression' complete with 0 errors, 1 warnings, and 0 messages.
Запустите следующую команду, чтобы сгенерировать HDL-код.
makehdl(systemname,'TargetDirectory',workingdir);
### Generating HDL for 'ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network'.
### Using the config set for model <a href="matlab:configset.showParameterGroup('ex_fxpdemo_neuralnet_regression', { 'HDL Code Generation' } )">ex_fxpdemo_neuralnet_regression</a> for HDL code generation parameters.
### Starting HDL check.
### Begin VHDL Code Generation for 'ex_fxpdemo_neuralnet_regression'.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1/Delays 1 as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Delays_1.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1/IW{1,1} as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\IW_1_1.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1/tansig/Approximate/Source as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Source.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1/tansig/Approximate as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Approximate.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1/tansig as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\tansig.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 1 as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Layer_1.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 2/Delays 1 as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Delays_1_block.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 2/LW{2,1} as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\LW_2_1.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 2/purelin as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\purelin.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Layer 2 as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Layer_2.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Process Input 1/mapminmax as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\mapminmax.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Process Input 1 as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Process_Input_1.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Process Output 1/mapminmax_reverse as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\mapminmax_reverse.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network/Process Output 1 as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Process_Output_1.vhd.
### Working on ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Function_Fitting_Neural_Network.vhd.
### Generating package file C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Function_Fitting_Neural_Network_pkg.vhd.
### Creating HDL Code Generation Check Report file://C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Function_Fitting_Neural_Network_report.html
### HDL check for 'ex_fxpdemo_neuralnet_regression' complete with 0 errors, 1 warnings, and 0 messages.
### HDL code generation complete.
Запустите следующую команду, чтобы сгенерировать испытательный стенд.
makehdltb(systemname,'TargetDirectory',workingdir);
### Begin TestBench generation. ### Generating HDL TestBench for 'ex_fxpdemo_neuralnet_regression/Function Fitting Neural Network'. ### Begin simulation of the model 'gm_ex_fxpdemo_neuralnet_regression'... ### Collecting data... ### Generating test bench data file: C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Input.dat. ### Generating test bench data file: C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Output_expected.dat. ### Working on Function_Fitting_Neural_Network_tb as C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Function_Fitting_Neural_Network_tb.vhd. ### Generating package file C:\Users\dorrubin\AppData\Local\Temp\tp37e308f4_fdd8_43a1_9a51_9b160cd7f145\ex_fxpdemo_neuralnet_regression\Function_Fitting_Neural_Network_tb_pkg.vhd. ### HDL TestBench generation complete.