diff --git a/python/tvm/autotvm/tuner/__init__.py b/python/tvm/autotvm/tuner/__init__.py
index dd53862d7dd2..437a97041d91 100644
--- a/python/tvm/autotvm/tuner/__init__.py
+++ b/python/tvm/autotvm/tuner/__init__.py
@@ -28,4 +28,4 @@
 from .index_based_tuner import GridSearchTuner, RandomTuner
 from .ga_tuner import GATuner
 from .xgboost_tuner import XGBTuner
-from .droplet_turner import DropletTuner
+from .droplet_tuner import DropletTuner
diff --git a/python/tvm/autotvm/tuner/droplet_turner.py b/python/tvm/autotvm/tuner/droplet_tuner.py
similarity index 88%
rename from python/tvm/autotvm/tuner/droplet_turner.py
rename to python/tvm/autotvm/tuner/droplet_tuner.py
index 35bc7d857f96..d58bfa49896b 100644
--- a/python/tvm/autotvm/tuner/droplet_turner.py
+++ b/python/tvm/autotvm/tuner/droplet_tuner.py
@@ -17,6 +17,7 @@
 """Tuner with droplet algorithm"""
 
 import logging
+import os
 import numpy as np
 from scipy import stats
 from .tuner import Tuner
@@ -44,13 +45,15 @@ def __init__(self, task, start_position=None, pvalue=0.05):
 
         for _, v in self.space.space_map.items():
             self.dims.append(len(v))
+        if len(self.dims) == 0:
+            self.dims.append(1)
 
         # start position
         start_position = [0] * len(self.dims) if start_position is None else start_position
         self.best_choice = (-1, [0] * len(self.dims), [99999])
         self.visited = set([self.space.knob2point(start_position)])
-        self.execution, self.total_execution, self.batch = 1, max(self.dims), 16
-        self.pvalue, self.step = pvalue, 1
+        self.execution, self.total_execution, self.pvalue = 1, max(self.dims), pvalue
+        self.step, self.iter, self.batch = 1, 0, max(16, os.cpu_count())
         self.next = [(self.space.knob2point(start_position), start_position)]
 
     def num_to_bin(self, value, factor=1):
@@ -100,7 +103,7 @@ def speculation(self):
             self.next += self.next_pos(self.search_space(self.execution))
 
     def update(self, inputs, results):
-        found_best_pos = False
+        found_best_pos, count_valids = False, 0
         for i, (_, res) in enumerate(zip(inputs, results)):
             try:
                 if np.mean(self.best_choice[2]) > np.mean(res.costs) and self.p_value(
@@ -108,6 +111,7 @@ def update(self, inputs, results):
                 ):
                     self.best_choice = (self.next[i][0], self.next[i][1], res.costs)
                     found_best_pos = True
+                count_valids += 1
             except TypeError:
                 LOGGER.debug("Solution is not valid")
                 continue
@@ -119,6 +123,13 @@ def update(self, inputs, results):
             self.next += self.next_pos(self.search_space())
             self.execution = 1
         self.speculation()
+        # stop, because all neighborhoods are invalid.
+        if count_valids == 0 and self.iter > 3:
+            self.next = []
+            LOGGER.warning(
+                f"Warning: early termination due to an all-invalid neighborhood \
+                after {self.iter} iterations"
+            )
 
     def has_next(self):
         return len(self.next) > 0