diff --git a/analysis/analysis.py b/analysis/analysis.py
index b08cbe8..ce7284e 100644
--- a/analysis/analysis.py
+++ b/analysis/analysis.py
@@ -27,6 +27,8 @@ def filestring_to_hardware(filestring):
         hardware = 'CPU'
     elif 'gpu' in filestring:
         hardware = 'GPU'
+    elif 'ncs2' in filestring:
+        hardware = 'NCS2'
     elif 'movidius' in filestring:
         hardware = 'MOVIDIUS'
     elif 'loihi' in filestring:
diff --git a/analysis/summary.py b/analysis/summary.py
index 5694915..b6947c7 100644
--- a/analysis/summary.py
+++ b/analysis/summary.py
@@ -64,7 +64,7 @@ def set_legend_title(ax):
 all_samples = idle_samples + run_samples
 
 dframe = pandas.DataFrame(all_samples)
-order = ['CPU', 'GPU', 'JETSON', 'MOVIDIUS', 'LOIHI']
+order = ['CPU', 'GPU', 'JETSON', 'NCS2', 'MOVIDIUS', 'LOIHI']
 
 # TABLE 1: Power, energy cost for all hardware devices
 
@@ -76,20 +76,21 @@ def set_legend_title(ax):
 
 # compute means over all samples for each hardware device
 mean_loihi = joules_dframe.loc[joules_dframe['hardware'] == 'LOIHI'].mean()
+mean_ncs2 = joules_dframe.loc[joules_dframe['hardware'] == 'NCS2'].mean()
 mean_movidius = joules_dframe.loc[joules_dframe['hardware'] == 'MOVIDIUS'].mean()
 mean_jetson = joules_dframe.loc[joules_dframe['hardware'] == 'JETSON'].mean()
 mean_cpu = joules_dframe.loc[joules_dframe['hardware'] == 'CPU'].mean()
 mean_gpu = joules_dframe.loc[joules_dframe['hardware'] == 'GPU'].mean()
 
 # print out mean values for populating table in summary document
-prefixes = ['loihi', 'movidius', 'jetson', 'cpu', 'gpu']
+prefixes = ['loihi', 'movidius', 'ncs2', 'jetson', 'cpu', 'gpu']
 
 print('Idle Power')
 for prefix in prefixes:
     print(prefix + ': %4f' % average_idle_power(idle_samples, prefix))
 
 print('')
-means = [mean_loihi, mean_movidius, mean_jetson, mean_cpu, mean_gpu]
+means = [mean_loihi, mean_movidius, mean_ncs2, mean_jetson, mean_cpu, mean_gpu]
 for data, prefix in zip(means, prefixes):
     print(prefix + ':')
     print('Total Power: %4f' % data['total_power'])
@@ -100,6 +101,7 @@ def set_legend_title(ax):
 
 # compute ratios for energy costs for plotting numbers alongside bars
 movidius_x = mean_movidius['dynamic_joules_per_inf'] / mean_loihi['dynamic_joules_per_inf']
+ncs_x = mean_ncs2['dynamic_joules_per_inf'] / mean_loihi['dynamic_joules_per_inf']
 jetson_x = mean_jetson['dynamic_joules_per_inf'] / mean_loihi['dynamic_joules_per_inf']
 cpu_x = mean_cpu['dynamic_joules_per_inf'] / mean_loihi['dynamic_joules_per_inf']
 gpu_x = mean_gpu['dynamic_joules_per_inf'] / mean_loihi['dynamic_joules_per_inf']
@@ -110,17 +112,19 @@ def set_legend_title(ax):
 plot = sns.barplot(
     'hardware', 'dynamic_joules_per_inf',
     data=joules_dframe,
-    order=['LOIHI', 'MOVIDIUS', 'JETSON', 'CPU', 'GPU'])
+    order=['LOIHI', 'MOVIDIUS', 'NCS2', 'JETSON', 'CPU', 'GPU'])
 
 # add ratios of power consumption to plot
 plt.gcf().text(
     0.18, 0.17, str(1) + 'x', fontsize=15, fontweight='bold')
 plt.gcf().text(
-    0.33, 0.17, str(round(movidius_x, 1)) + 'x', fontsize=15, fontweight='bold')
+    0.28, 0.17, str(round(movidius_x, 1)) + 'x', fontsize=15, fontweight='bold')
 plt.gcf().text(
-    0.47, 0.17, str(round(jetson_x, 1)) + 'x', fontsize=15, fontweight='bold')
+    0.41, 0.17, str(round(ncs_x, 1)) + 'x', fontsize=15, fontweight='bold')
 plt.gcf().text(
-    0.62, 0.17, str(round(cpu_x, 1)) + 'x', fontsize=15, fontweight='bold')
+    0.52, 0.17, str(round(jetson_x, 1)) + 'x', fontsize=15, fontweight='bold')
+plt.gcf().text(
+    0.65, 0.17, str(round(cpu_x, 1)) + 'x', fontsize=15, fontweight='bold')
 plt.gcf().text(
     0.77, 0.17, str(round(gpu_x, 1)) + 'x', fontsize=15, fontweight='bold')
 
@@ -131,7 +135,6 @@ def set_legend_title(ax):
 plot.figure.savefig("./paper/figures/per_inf_comparison.png")
 plt.show()
 
-
 # PLOT 2. Batchsize comparison
 fig, ax = plt.subplots(1, 2, figsize=(12, 6))
 fig.subplots_adjust(hspace=0.9)
diff --git a/models.py b/models.py
index 56c43d4..d4feea0 100644
--- a/models.py
+++ b/models.py
@@ -1,7 +1,7 @@
 import string
 import warnings
 import numpy as np
-# import tensorflow as tf
+import tensorflow as tf
 
 # attempt to import movidius V1 api, won't work in conda environment
 try:
@@ -34,9 +34,6 @@
     input_blob = next(iter(network.inputs))
     output_blob = next(iter(network.outputs))
 
-    print(network.batch_size)
-    print(network.precision)
-
 except ImportError:
     warnings.warn('NCS OpenVino API not installed', UserWarning)