diff --git a/tensorflow/lite/kernels/internal/BUILD b/tensorflow/lite/kernels/internal/BUILD
index bf329cd2d52..0f363f06a8c 100644
--- a/tensorflow/lite/kernels/internal/BUILD
+++ b/tensorflow/lite/kernels/internal/BUILD
@@ -175,7 +175,6 @@ cc_library(
     deps = [
         ":types",
         "@gemmlowp//:fixedpoint",
-        "@gemmlowp//:profiler",
     ] + select({
         ":haswell": tflite_deps_intel,
         ":ios_x86_64": tflite_deps_intel,
diff --git a/tensorflow/lite/kernels/internal/common.h b/tensorflow/lite/kernels/internal/common.h
index 71a306035c2..03d71f2b06e 100644
--- a/tensorflow/lite/kernels/internal/common.h
+++ b/tensorflow/lite/kernels/internal/common.h
@@ -46,7 +46,6 @@ limitations under the License.
 #endif
 
 #include "fixedpoint/fixedpoint.h"
-#include "profiling/instrumentation.h"
 #include "tensorflow/lite/kernels/internal/types.h"
 
 namespace tflite {
@@ -96,7 +95,6 @@ inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size,
   //   return (array.colwise() + bias).cwiseMin(clamp_max).cwiseMin(clamp_max).
   // This turned out to severely regress performance: +4ms (i.e. 8%) on
   // MobileNet v2 / 1.0 / 224. So we keep custom NEON code for now.
-  gemmlowp::ScopedProfilingLabel label("BiasAndClamp");
   TFLITE_DCHECK_EQ((array_size % bias_size), 0);
 #ifdef USE_NEON
   float* array_ptr = array_data;