В этом примере показано, как преобразовать модель регрессии нейронной сети в Simulink в фиксированную точку с помощью fxpopt function и Lookup Table Optimizer.
Fixed-Point Designer предоставляет рабочие процессы через Инструмент с фиксированной точкой, который может преобразовать проект из типов данных с плавающей точкой в типы данных с фиксированной точкой. The fxpopt функция оптимизирует типы данных в модели на основе заданных системных поведенческих ограничений. Для получения дополнительной информации обратитесь к ссылке на документацию https://www.mathworks.com/help/fixedpoint/ref/fxpopt.html Lookup Table Optimizer генерирует замены интерполяционной таблицы эффективности памяти для неограниченных функций, таких как exp и log2. Используя эти инструменты, в этом примере показано, как преобразовать обученную модель регрессии нейронной сети с плавающей точкой для использования встроенных эффективных типов данных с фиксированной точкой.
The engine_dataset содержит данные, представляющие зависимость между расходом топлива и скоростью двигателя, 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 обучающего инструмента и просмотра сети.
nnet.guis.closeAllViews();
nntraintool('close');
После обучения сети используйте gensim функция из набора Deep Learning Toolbox(TM), чтобы сгенерировать модель Simulink.
[sysName, netName] = gensim(net, 'Name', 'mTrainedNN');
Модель, сгенерированная gensim функция содержит нейронную сеть с обученными весами и смещениями. Чтобы подготовить эту сгенерированную модель для преобразования с фиксированной точкой, следуйте шагам подготовки в руководствах по лучшим 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 дополнительно модифицируется, чтобы обеспечить возможность регистрации сигналов на выходе сети, добавить входные входные стимулы и блоки верификации.
Откройте и осмотрите 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 точностью 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 для оптимизации типов данных в проектируемой системе и исследования решения. Программа анализирует область значений объектов в system_under_design и ограничения по длине слов и допуску, указанные в opts применить гетерогенные типы данных к модели с минимизацией общей ширины бита.
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 график точности регрессии модели с фиксированной точкой.
plotRegression(sim_out, baseline_output, system_under_design, 'Regression after conversion');
Функция активации Tanh на слое 1 может быть заменена либо интерполяционной таблицей, либо реализацией CORDIC для более эффективной генерации кода с фиксированной точкой. В этом примере мы будем использовать Lookup Table Optimizer, чтобы получить интерполяционную таблицу в качестве замены tanh. Мы будем использовать EvenPow2Spacing для более высокой скорости выполнения. Для получения дополнительной информации смотрите 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 замены функции
sim_out = sim(model, 'SaveFormat', 'Dataset');
Постройте график точности регрессии после замены функции.
plotRegression(sim_out, baseline_output, system_under_design, 'Regression after function replacement');
Для генерации HDL-кода требуется лицензия HDL- Coder™.
Выберите модель, для которой нужно 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 для проверки совместимости 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.
Выполните следующую команду для генерации HDL-кода.
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.
Выполните 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.