diff --git a/superbench/analyzer/generate_statistic.py b/superbench/analyzer/generate_statistic.py
index 13cd146f9..80ccaa8f8 100644
--- a/superbench/analyzer/generate_statistic.py
+++ b/superbench/analyzer/generate_statistic.py
@@ -5,6 +5,7 @@
 
 import argparse
 import os
+import natsort as ns
 
 from joblib import Parallel, delayed
 import pandas as pd
@@ -72,6 +73,8 @@ def output_excel(self, excel_file, stat_df, digit=2):
 
             for benchmark in self._benchmark_metrics_dict:
                 benchmark_df = stat_df[self._benchmark_metrics_dict[benchmark]]
+                #benchmark_df = benchmark_df[,mixedsort(names(benchmark_df))]
+                benchmark_df = benchmark_df.reindex(ns.natsorted(benchmark_df.columns), axis=1)
                 sheet_name = benchmark if len(benchmark) <= 30 else benchmark.split('-')[-1]
                 benchmark_df.to_excel(writer, sheet_name=sheet_name)
                 worksheet = writer.sheets[sheet_name]
diff --git a/superbench/analyzer/rules/aggregation_rules.yaml b/superbench/analyzer/rules/aggregation_rules.yaml
index db8e7a87d..3b330b5bb 100644
--- a/superbench/analyzer/rules/aggregation_rules.yaml
+++ b/superbench/analyzer/rules/aggregation_rules.yaml
@@ -2,7 +2,7 @@
 version: v0.5
 superbench:
   rules:
-    model-benchmarks:
+    model-train-benchmarks:
       statistics:
         - mean
       categories: model
@@ -14,6 +14,13 @@ superbench:
         - resnet_models/pytorch-resnet\d*/.*_train_throughput
         - vgg_models/pytorch-vgg\d*/.*_train_throughput
         - densenet_models/.*/.*_train_throughput
+    model-inference-benchmarks:
+      statistics:
+        - mean
+      categories: model
+      aggregate: True
+      metrics:
+        - model-benchmarks:.*/.*/.*_inference_throughput:\d+
     micro-aggregation:
       statistics: mean
       categories: MICRO1
diff --git a/superbench/analyzer/rules/diagnosis_rules.yaml b/superbench/analyzer/rules/diagnosis_rules.yaml
index 8ece570ab..3771566d3 100644
--- a/superbench/analyzer/rules/diagnosis_rules.yaml
+++ b/superbench/analyzer/rules/diagnosis_rules.yaml
@@ -191,4 +191,41 @@ superbench:
       categories: VGG
       function: multi_rules
       criteria: 'lambda label:True if label["vgg_throughput"]>=2 else False'
-
+    model_inference_throughput_rule:
+      function: variance
+      criteria: 'lambda x:x<-0.05'
+      categories: Model
+      metrics:
+        - model-benchmarks:.*/.*bert.*/.*_inference_throughput
+        - model-benchmarks:.*/.*gpt.*/.*_inference_throughput
+        - model-benchmarks:.*/.*lstm.*/.*_inference_throughput
+  # Rule 8: If 2+ CNN models suffer > 5% downgrade, label it as Not acceptable
+    resnet_inference_throughput:
+      function: variance
+      criteria: 'lambda x:x<-0.05'
+      store: true
+      categories: CNN
+      metrics:
+        - model-benchmarks:.*/.*resnet.*/.*_inference_throughput
+    vgg_inference_throughput:
+      function: variance
+      criteria: 'lambda x:x<-0.05'
+      store: true
+      categories: CNN
+      metrics:
+        - model-benchmarks:.*/.*vgg.*/.*_inference_throughput
+    densenet_inference_throughput:
+      function: variance
+      criteria: 'lambda x:x<-0.05'
+      store: true
+      categories: CNN
+      metrics:
+        - model-benchmarks:.*/.*densenet.*/.*_inference_throughput
+    cnn_inference_throughput_rule:
+      categories: CNN
+      function: multi_rules
+      criteria: 'lambda label:True if label["resnet_inference_throughput"]+label["densenet_inference_throughput"]>=2 else False'
+    vgg_inference_throughput_rule:
+      categories: VGG
+      function: multi_rules
+      criteria: 'lambda label:True if label["vgg_inference_throughput"]>=2 else False'