diff --git a/compiler/characterizer/neural_network.py b/compiler/characterizer/neural_network.py
new file mode 100644
index 00000000..94da9a66
--- /dev/null
+++ b/compiler/characterizer/neural_network.py
@@ -0,0 +1,150 @@
+# See LICENSE for licensing information.
+#
+# Copyright (c) 2016-2019 Regents of the University of California and The Board
+# of Regents for the Oklahoma Agricultural and Mechanical College
+# (acting for and on behalf of Oklahoma State University)
+# All rights reserved.
+#
+
+from .analytical_util import *
+from .simulation import simulation
+from globals import OPTS
+import debug
+
+import os
+import math
+import numpy as np
+from tensorflow import keras
+from tensorflow.keras import layers
+import tensorflow as tf
+
+relative_data_path = "/sim_data"
+data_fnames = ["rise_delay.csv",
+               "fall_delay.csv",
+               "rise_slew.csv",
+               "fall_slew.csv",
+               "write1_power.csv",
+               "write0_power.csv",
+               "read1_power.csv",
+               "read0_power.csv",
+               "leakage_data.csv"]
+               
+data_dir = OPTS.openram_tech+relative_data_path
+data_paths = [data_dir +'/'+fname for fname in data_fnames]
+
+class neural_network(simulation):
+
+    def __init__(self, sram, spfile, corner):
+        super().__init__(sram, spfile, corner)
+        self.set_corner(corner)
+
+    def get_lib_values(self, slews, loads):
+        """
+        A model and prediction is created for each output needed for the LIB 
+        """
+
+        log_num_words = math.log(OPTS.num_words, 2)
+        debug.info(1, "OPTS.words_per_row={}".format(OPTS.words_per_row))
+        model_inputs = [log_num_words, 
+                        OPTS.word_size, 
+                        OPTS.words_per_row, 
+                        self.sram.width * self.sram.height,
+                        process_transform[self.process], 
+                        self.vdd_voltage, 
+                        self.temperature]  
+
+        self.create_measurement_names()
+        models = self.train_models()
+
+        # Set delay/power for slews and loads
+        port_data = self.get_empty_measure_data_dict()
+        debug.info(1, 'Slew, Load, Delay(ns), Slew(ns)')
+        max_delay = 0.0
+        for slew in slews:
+            for load in loads:
+                # List returned with value order being delay, power, leakage, slew
+                # FIXME: make order less hard coded
+                sram_vals = self.get_predictions(model_inputs+[slew, load], models)
+                # Delay is only calculated on a single port and replicated for now.
+                for port in self.all_ports:
+                    port_data[port]['delay_lh'].append(sram_vals[0][0][0])
+                    port_data[port]['delay_hl'].append(sram_vals[1][0][0])
+                    port_data[port]['slew_lh'].append(sram_vals[2][0][0])
+                    port_data[port]['slew_hl'].append(sram_vals[3][0][0])
+                    
+                    port_data[port]['write1_power'].append(sram_vals[4][0][0])
+                    port_data[port]['write0_power'].append(sram_vals[5][0][0])
+                    port_data[port]['read1_power'].append(sram_vals[6][0][0])
+                    port_data[port]['read0_power'].append(sram_vals[7][0][0])
+                    
+                    # Disabled power not modeled. Copied from other power predictions
+                    port_data[port]['disabled_write1_power'].append(sram_vals[4][0][0])
+                    port_data[port]['disabled_write0_power'].append(sram_vals[5][0][0])
+                    port_data[port]['disabled_read1_power'].append(sram_vals[6][0][0])
+                    port_data[port]['disabled_read0_power'].append(sram_vals[7][0][0])
+                        
+
+        # Estimate the period as double the delay with margin
+        period_margin = 0.1
+        sram_data = {"min_period": sram_vals[0][0][0] * 2,
+                     "leakage_power": sram_vals[8][0][0]}
+
+        debug.info(2, "SRAM Data:\n{}".format(sram_data))
+        debug.info(2, "Port Data:\n{}".format(port_data))
+
+        return (sram_data, port_data)
+
+    def get_predictions(self, model_inputs, models): 
+        """
+        Generate a model and prediction for LIB output
+        """
+               
+        scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_paths[0])])
+
+        predictions = []
+        for m, path in zip(models, data_paths):
+            features, labels = get_scaled_data(path)
+            scaled_pred = self.model_prediction(m, scaled_inputs)
+            pred = unscale_data(scaled_pred.tolist(), path)
+            debug.info(1,"Unscaled Prediction = {}".format(pred))
+            predictions.append(pred)
+        return predictions
+
+    def train_models(self):
+        """
+        Generate and return models
+        """
+        models = []
+        for path in data_paths:
+            features, labels = get_scaled_data(path)
+            model = self.generate_model(features, labels)
+            models.append(model)
+        return models
+
+
+    def generate_model(self, features, labels):
+        """
+        Supervised training of model.
+        """
+        
+        model = keras.Sequential([
+            layers.Dense(32, activation=tf.nn.relu, input_shape=[features.shape[1]]),
+            layers.Dense(32, activation=tf.nn.relu),
+            layers.Dense(32, activation=tf.nn.relu),
+            layers.Dense(1)
+        ])
+
+        optimizer = keras.optimizers.RMSprop(0.0099)
+        model.compile(loss='mean_squared_error', optimizer=optimizer)
+        model.fit(features, labels, epochs=100, verbose=0)
+        return model
+        
+    def model_prediction(self, model, features):    
+        """
+        Have the model perform a prediction and unscale the prediction
+        as the model is trained with scaled values.
+        """
+        
+        pred = model.predict(features)
+        return pred
+        
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