diff --git a/predtuner/pipedbin.py b/predtuner/pipedbin.py
index b5d525ddf8e81f53271afde486f43a597b26a1a5..8882370bb4fefeb430000809f909b34dbea62680 100644
--- a/predtuner/pipedbin.py
+++ b/predtuner/pipedbin.py
@@ -92,11 +92,11 @@ class PipedBinaryApp(ModeledApp):
     def empirical_measure_qos_cost(
         self, with_approxes: KnobsT, is_test: bool
     ) -> Tuple[float, float]:
-        from time import time_ns
+        from time import time
 
-        time_begin = time_ns() / (10 ** 9)
+        time_begin = time()
         _, qos = self._run_on_knobs(with_approxes, is_test)
-        time_end = time_ns() / (10 ** 9)
+        time_end = time()
         return qos, time_end - time_begin
 
     def get_models(self) -> List[Union["IPerfModel", "IQoSModel"]]:
diff --git a/predtuner/torchapp.py b/predtuner/torchapp.py
index 33ceaad991b005d632b48497230ee28744c9b2df..92ca90cb37cc3ea7a01630411f6a6541b4d4eba9 100644
--- a/predtuner/torchapp.py
+++ b/predtuner/torchapp.py
@@ -172,7 +172,7 @@ class TorchApp(ModeledApp, abc.ABC):
         """Measure the QoS and performance of Module with given approximation
         empirically (i.e., by running the Module on the dataset)."""
 
-        from time import time_ns
+        from time import time
         from tqdm import tqdm
 
         dataloader = self.test_loader if is_test else self.tune_loader
@@ -181,13 +181,13 @@ class TorchApp(ModeledApp, abc.ABC):
         approxed = self._apply_knobs(with_approxes)
         qoses = []
 
-        time_begin = time_ns() / (10 ** 9)
+        time_begin = time()
         for inputs, targets in dataloader:
             inputs = move_to_device_recursively(inputs, self.device)
             targets = move_to_device_recursively(targets, self.device)
             outputs = approxed(inputs)
             qoses.append(self.tensor_to_qos(outputs, targets))
-        time_end = time_ns() / (10 ** 9)
+        time_end = time()
         qos = float(self.combine_qos(np.array(qoses)))
         return qos, time_end - time_begin