adding giuseppes model

keras-to-hls/example-keras-model-files/2layer_100x100.json

+{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "input_1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 100], "dtype": "float32", "sparse": false, "name": "input_1"}, "inbound_nodes": []}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 100, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 100, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_1", 0, 0, {}]]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["dense_2", 0, 0]]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file

keras-to-hls/keras-config.yml

-#KerasJson: example-keras-model-files/KERAS_3layer.json
-#KerasH5:   example-keras-model-files/KERAS_3layer_weights.h5
-KerasJson: example-keras-model-files/KERAS_dense_16x100x100x100x100x100x5.json
-KerasH5:   example-keras-model-files/KERAS_dense_16x100x100x100x100x100x5_weights.h5
-OutputDir: hls-6layer100s-reuse100
+# KerasJson: example-keras-model-files/KERAS_1layer.json
+# KerasH5:   example-keras-model-files/KERAS_1layer_weights.h5
+KerasJson: example-keras-model-files/KERAS_3layer.json
+KerasH5:   example-keras-model-files/KERAS_3layer_weights.h5
+#KerasJson: example-keras-model-files/KERAS_dense_16x100x100x100x100x100x5.json
+#KerasH5:   example-keras-model-files/KERAS_dense_16x100x100x100x100x100x5_weights.h5
+#OutputDir: hls-6layer100s-reuse100
+OutputDir: hls-3layer-reuse9-noalloc-noreshape
 ProjectName: myproject
 XilinxPart: xcku115-flvb2104-2-i
 ClockPeriod: 5
 
 IOType: io_parallel # options: io_serial/io_parallel
-ReuseFactor: 100
+ReuseFactor: 9
 DefaultPrecision: ap_fixed<16,6> 

nnet_utils/nnet_layer.h

@@ -73,12 +73,14 @@ void compute_layer(
 
     // Replace ceil function with home-made macro prevents Vivado 2018.2 segfault
     int multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, CONFIG_T::reuse_factor);
-    #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+    // #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
 
     // Workaround the above restriction.
     #pragma HLS ARRAY_RESHAPE variable=weights block factor=multiplier_limit
     #pragma HLS ARRAY_RESHAPE variable=mult block factor=multiplier_limit
-    
+    // #pragma HLS ARRAY_PARTITION variable=weights block factor=multiplier_limit
+    // #pragma HLS ARRAY_PARTITION variable=mult    block factor=multiplier_limit
+
     // Do the matrix-multiply
     // Product1: for(int ii = 0; ii < CONFIG_T::n_in; ii++) {