diff --git a/configure.py b/configure.py
index ff615a7..15299b8 100644
--- a/configure.py
+++ b/configure.py
@@ -60,7 +60,8 @@
 APPLE_BAZEL_FILES = [
     'tensorflow/lite/experimental/ios/BUILD',
     'tensorflow/lite/experimental/objc/BUILD',
-    'tensorflow/lite/experimental/swift/BUILD'
+    'tensorflow/lite/experimental/swift/BUILD',
+    'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
 ]
 
 # List of files to move when building for iOS.
diff --git a/tensorflow/lite/tools/benchmark/experimental/c/BUILD b/tensorflow/lite/tools/benchmark/experimental/c/BUILD
new file mode 100644
index 0000000..28bbd3f
--- /dev/null
+++ b/tensorflow/lite/tools/benchmark/experimental/c/BUILD
@@ -0,0 +1,30 @@
+load("//tensorflow/lite:build_def.bzl", "tflite_copts")
+
+package(
+    default_visibility = ["benchmark"],
+    licenses = ["notice"],  # Apache 2.0
+)
+
+package_group(
+    name = "benchmark",
+    packages = [
+        "//tensorflow/lite/tools/benchmark/...",
+    ],
+)
+
+cc_library(
+    name = "benchmark_c_api",
+    srcs = ["benchmark_c_api.cc"],
+    hdrs = [
+        "benchmark_c_api.h",
+        "c_api_types.h",
+    ],
+    copts = tflite_copts(),
+    visibility = [
+        "benchmark",
+    ],
+    deps = [
+        "//tensorflow/core:stats_calculator_portable",
+        "//tensorflow/lite/tools/benchmark:benchmark_tflite_model_lib",
+    ],
+)
diff --git a/tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.cc b/tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.cc
new file mode 100644
index 0000000..d6fb393
--- /dev/null
+++ b/tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.cc
@@ -0,0 +1,175 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.h"
+
+#include "tensorflow/core/util/stats_calculator.h"
+#include "tensorflow/lite/tools/benchmark/benchmark_tflite_model.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tflite::benchmark::BenchmarkResults type.
+// -----------------------------------------------------------------------------
+struct TfLiteBenchmarkResults {
+  const tflite::benchmark::BenchmarkResults* results;
+};
+
+// Converts the given int64_t stat into a TfLiteBenchmarkInt64Stat struct.
+TfLiteBenchmarkInt64Stat ConvertStat(const tensorflow::Stat<int64_t>& stat) {
+  return {
+      stat.empty(),    stat.first(), stat.newest(),        stat.max(),
+      stat.min(),      stat.count(), stat.sum(),           stat.squared_sum(),
+      stat.all_same(), stat.avg(),   stat.std_deviation(),
+  };
+}
+
+TfLiteBenchmarkInt64Stat TfLiteBenchmarkResultsGetInferenceTimeMicroseconds(
+    const TfLiteBenchmarkResults* results) {
+  return ConvertStat(results->results->inference_time_us());
+}
+
+TfLiteBenchmarkInt64Stat TfLiteBenchmarkResultsGetWarmupTimeMicroseconds(
+    const TfLiteBenchmarkResults* results) {
+  return ConvertStat(results->results->warmup_time_us());
+}
+
+int64_t TfLiteBenchmarkResultsGetStartupLatencyMicroseconds(
+    const TfLiteBenchmarkResults* results) {
+  return results->results->startup_latency_us();
+}
+
+uint64_t TfLiteBenchmarkResultsGetInputBytes(
+    const TfLiteBenchmarkResults* results) {
+  return results->results->input_bytes();
+}
+
+double TfLiteBenchmarkResultsGetThroughputMbPerSecond(
+    const TfLiteBenchmarkResults* results) {
+  return results->results->throughput_MB_per_second();
+}
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tflite::benchmark::BenchmarkListener type.
+// -----------------------------------------------------------------------------
+class BenchmarkListenerAdapter : public tflite::benchmark::BenchmarkListener {
+ public:
+  void OnBenchmarkStart(
+      const tflite::benchmark::BenchmarkParams& params) override {
+    if (on_benchmark_start_fn_ != nullptr) {
+      on_benchmark_start_fn_();
+    }
+  }
+
+  void OnSingleRunStart(tflite::benchmark::RunType runType) override {
+    if (on_single_run_start_fn_ != nullptr) {
+      on_single_run_start_fn_(runType == tflite::benchmark::WARMUP
+                                  ? TfLiteBenchmarkWarmup
+                                  : TfLiteBenchmarkRegular);
+    }
+  }
+
+  void OnSingleRunEnd() override {
+    if (on_single_run_end_fn_ != nullptr) {
+      on_single_run_end_fn_();
+    }
+  }
+
+  void OnBenchmarkEnd(
+      const tflite::benchmark::BenchmarkResults& results) override {
+    if (on_benchmark_end_fn_ != nullptr) {
+      TfLiteBenchmarkResults* wrapper = new TfLiteBenchmarkResults{&results};
+      on_benchmark_end_fn_(wrapper);
+      delete wrapper;
+    }
+  }
+
+  // Function pointers set by the TfLiteBenchmarkListenerSetCallbacks call.
+  // Only non-null callbacks will be actually called.
+  void (*on_benchmark_start_fn_)();
+  void (*on_single_run_start_fn_)(TfLiteBenchmarkRunType runType);
+  void (*on_single_run_end_fn_)();
+  void (*on_benchmark_end_fn_)(TfLiteBenchmarkResults* results);
+};
+
+struct TfLiteBenchmarkListener {
+  std::unique_ptr<BenchmarkListenerAdapter> adapter;
+};
+
+TfLiteBenchmarkListener* TfLiteBenchmarkListenerCreate() {
+  std::unique_ptr<BenchmarkListenerAdapter> adapter(
+      new BenchmarkListenerAdapter());
+  return new TfLiteBenchmarkListener{std::move(adapter)};
+}
+
+void TfLiteBenchmarkListenerDelete(TfLiteBenchmarkListener* listener) {
+  delete listener;
+}
+
+void TfLiteBenchmarkListenerSetCallbacks(
+    TfLiteBenchmarkListener* listener, void (*on_benchmark_start_fn)(),
+    void (*on_single_run_start_fn)(TfLiteBenchmarkRunType runType),
+    void (*on_single_run_end_fn)(),
+    void (*on_benchmark_end_fn)(TfLiteBenchmarkResults* results)) {
+  listener->adapter->on_benchmark_start_fn_ = on_benchmark_start_fn;
+  listener->adapter->on_single_run_start_fn_ = on_single_run_start_fn;
+  listener->adapter->on_single_run_end_fn_ = on_single_run_end_fn;
+  listener->adapter->on_benchmark_end_fn_ = on_benchmark_end_fn;
+}
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tflite::benchmark::BenchmarkTfLiteModel type.
+// -----------------------------------------------------------------------------
+struct TfLiteBenchmarkTfLiteModel {
+  std::unique_ptr<tflite::benchmark::BenchmarkTfLiteModel> benchmark_model;
+};
+
+TfLiteBenchmarkTfLiteModel* TfLiteBenchmarkTfLiteModelCreate() {
+  std::unique_ptr<tflite::benchmark::BenchmarkTfLiteModel> benchmark_model(
+      new tflite::benchmark::BenchmarkTfLiteModel());
+  return new TfLiteBenchmarkTfLiteModel{std::move(benchmark_model)};
+}
+
+void TfLiteBenchmarkTfLiteModelDelete(
+    TfLiteBenchmarkTfLiteModel* benchmark_model) {
+  delete benchmark_model;
+}
+
+TfLiteStatus TfLiteBenchmarkTfLiteModelInit(
+    TfLiteBenchmarkTfLiteModel* benchmark_model) {
+  return benchmark_model->benchmark_model->Init();
+}
+
+TfLiteStatus TfLiteBenchmarkTfLiteModelRun(
+    TfLiteBenchmarkTfLiteModel* benchmark_model) {
+  return benchmark_model->benchmark_model->Run();
+}
+
+TfLiteStatus TfLiteBenchmarkTfLiteModelRunWithArgs(
+    TfLiteBenchmarkTfLiteModel* benchmark_model, int argc, char** argv) {
+  return benchmark_model->benchmark_model->Run(argc, argv);
+}
+
+void TfLiteBenchmarkTfLiteModelAddListener(
+    TfLiteBenchmarkTfLiteModel* benchmark_model,
+    const TfLiteBenchmarkListener* listener) {
+  return benchmark_model->benchmark_model->AddListener(listener->adapter.get());
+}
+
+#ifdef __cplusplus
+}
+#endif  // __cplusplus
diff --git a/tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.h b/tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.h
new file mode 100644
index 0000000..a642a4f
--- /dev/null
+++ b/tensorflow/lite/tools/benchmark/experimental/c/benchmark_c_api.h
@@ -0,0 +1,131 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_TOOLS_BENCHMARK_EXPERIMENTAL_C_BENCHMARK_C_API_H_
+#define TENSORFLOW_LITE_TOOLS_BENCHMARK_EXPERIMENTAL_C_BENCHMARK_C_API_H_
+
+#include "c_api_types.h"
+
+// -----------------------------------------------------------------------------
+// Experimental C APIs for the benchmark tool, mainly intended to be used for
+// building a standalone TensorFlow Lite benchmark framework for iOS. This
+// header only has a minimal dependency to the C API types, which can be
+// included in the framework itself.
+// -----------------------------------------------------------------------------
+
+#ifdef __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+typedef enum {
+  TfLiteBenchmarkWarmup,
+  TfLiteBenchmarkRegular,
+} TfLiteBenchmarkRunType;
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tensorflow::Stat<int64_t> type.
+// -----------------------------------------------------------------------------
+typedef struct TfLiteBenchmarkInt64Stat {
+  bool empty;
+  int64_t first;
+  int64_t newest;
+  int64_t max;
+  int64_t min;
+  int64_t count;
+  int64_t sum;
+  double squared_sum;
+  bool all_same;
+  double avg;
+  int64_t std_deviation;
+} TfLiteBenchmarkInt64Stat;
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tflite::benchmark::BenchmarkResults type.
+// -----------------------------------------------------------------------------
+typedef struct TfLiteBenchmarkResults TfLiteBenchmarkResults;
+
+extern TfLiteBenchmarkInt64Stat
+TfLiteBenchmarkResultsGetInferenceTimeMicroseconds(
+    const TfLiteBenchmarkResults* results);
+
+extern TfLiteBenchmarkInt64Stat TfLiteBenchmarkResultsGetWarmupTimeMicroseconds(
+    const TfLiteBenchmarkResults* results);
+
+extern int64_t TfLiteBenchmarkResultsGetStartupLatencyMicroseconds(
+    const TfLiteBenchmarkResults* results);
+
+extern uint64_t TfLiteBenchmarkResultsGetInputBytes(
+    const TfLiteBenchmarkResults* results);
+
+extern double TfLiteBenchmarkResultsGetThroughputMbPerSecond(
+    const TfLiteBenchmarkResults* results);
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tflite::benchmark::BenchmarkListener type.
+// -----------------------------------------------------------------------------
+typedef struct TfLiteBenchmarkListener TfLiteBenchmarkListener;
+
+extern TfLiteBenchmarkListener* TfLiteBenchmarkListenerCreate();
+
+extern void TfLiteBenchmarkListenerDelete(TfLiteBenchmarkListener* listener);
+
+// Sets the listener callbacks. Only non-null callback functions will be called
+// when the following events occur.
+//
+// - on_benchmark_start: Called before the (outer) inference loop begins. Note
+//     that this is called *after* the interpreter has been initialized, but
+//     *before* any warmup runs have been executed.
+// - on_single_run_start: Called before a single (inner) inference call starts.
+// - on_single_run_end: Called before a single (inner) inference call ends.
+// - on_benchmark_end: Called after the (outer) inference loop ends.
+//
+// In case of `on_benchmark_end` callback, the passed in `results` pointer is
+// only valid during the callback function execution, and will be destroyed
+// afterwards.
+extern void TfLiteBenchmarkListenerSetCallbacks(
+    TfLiteBenchmarkListener* listener, void (*on_benchmark_start_fn)(),
+    void (*on_single_run_start_fn)(TfLiteBenchmarkRunType runType),
+    void (*on_single_run_end_fn)(),
+    void (*on_benchmark_end_fn)(TfLiteBenchmarkResults* results));
+
+// -----------------------------------------------------------------------------
+// C APIs corresponding to tflite::benchmark::BenchmarkTfLiteModel type.
+// -----------------------------------------------------------------------------
+typedef struct TfLiteBenchmarkTfLiteModel TfLiteBenchmarkTfLiteModel;
+
+// TODO(b/144321502): Support BenchmarkParams.
+extern TfLiteBenchmarkTfLiteModel* TfLiteBenchmarkTfLiteModelCreate();
+
+extern void TfLiteBenchmarkTfLiteModelDelete(
+    TfLiteBenchmarkTfLiteModel* benchmark_model);
+
+extern TfLiteStatus TfLiteBenchmarkTfLiteModelInit(
+    TfLiteBenchmarkTfLiteModel* benchmark_model);
+
+extern TfLiteStatus TfLiteBenchmarkTfLiteModelRun(
+    TfLiteBenchmarkTfLiteModel* benchmark_model);
+
+extern TfLiteStatus TfLiteBenchmarkTfLiteModelRunWithArgs(
+    TfLiteBenchmarkTfLiteModel* benchmark_model, int argc, char** argv);
+
+extern void TfLiteBenchmarkTfLiteModelAddListener(
+    TfLiteBenchmarkTfLiteModel* benchmark_model,
+    const TfLiteBenchmarkListener* listener);
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif  // __cplusplus
+
+#endif  // TENSORFLOW_LITE_TOOLS_BENCHMARK_EXPERIMENTAL_C_BENCHMARK_C_API_H_
diff --git a/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h b/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h
new file mode 100644
index 0000000..9b81209
--- /dev/null
+++ b/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h
@@ -0,0 +1,629 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// This file defines a C API for implementing operations in tflite.
+// These operations can be defined using c++ but the interface between
+// the interpreter and the operations are C.
+//
+// Summary of abstractions
+// TF_LITE_ENSURE - Self-sufficient error checking
+// TfLiteStatus - Status reporting
+// TfLiteIntArray - stores tensor shapes (dims),
+// TfLiteContext - allows an op to access the tensors
+// TfLiteTensor - tensor (a multidimensional array)
+// TfLiteNode - a single node or operation
+// TfLiteRegistration - the implementation of a conceptual operation.
+//
+// Some abstractions in this file are created and managed by Interpreter.
+#ifndef TENSORFLOW_LITE_C_C_API_INTERNAL_H_
+#define TENSORFLOW_LITE_C_C_API_INTERNAL_H_
+
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+typedef enum { kTfLiteOk = 0, kTfLiteError = 1 } TfLiteStatus;
+
+// The list of external context types known to TF Lite. This list exists solely
+// to avoid conflicts and to ensure ops can share the external contexts they
+// need. Access to the external contexts is controled by one of the
+// corresponding support files.
+typedef enum {
+  kTfLiteEigenContext = 0,       // include eigen_support.h to use.
+  kTfLiteGemmLowpContext = 1,    // include gemm_support.h to use.
+  kTfLiteEdgeTpuContext = 2,     // Placeholder for Edge TPU support.
+  kTfLiteCpuBackendContext = 3,  // include cpu_backend_support.h to use.
+  kTfLiteMaxExternalContexts = 4
+} TfLiteExternalContextType;
+
+// Forward declare so dependent structs and methods can reference these types
+// prior to the struct definitions.
+struct TfLiteContext;
+struct TfLiteDelegate;
+struct TfLiteRegistration;
+
+// An external context is a collection of information unrelated to the TF Lite
+// framework, but useful to a subset of the ops. TF Lite knows very little
+// about about the actual contexts, but it keeps a list of them, and is able to
+// refresh them if configurations like the number of recommended threads
+// change.
+typedef struct {
+  TfLiteExternalContextType type;
+  TfLiteStatus (*Refresh)(struct TfLiteContext* context);
+} TfLiteExternalContext;
+
+#define kOptionalTensor (-1)
+
+// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
+// indices
+typedef struct {
+  int size;
+// gcc 6.1+ have a bug where flexible members aren't properly handled
+// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
+#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
+    __GNUC_MINOR__ >= 1
+  int data[0];
+#else
+  int data[];
+#endif
+} TfLiteIntArray;
+
+// Given the size (number of elements) in a TfLiteIntArray, calculate its size
+// in bytes.
+int TfLiteIntArrayGetSizeInBytes(int size);
+
+// Create a array of a given `size` (uninitialized entries).
+// This returns a pointer, that you must free using TfLiteIntArrayFree().
+TfLiteIntArray* TfLiteIntArrayCreate(int size);
+
+// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise.
+int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b);
+
+// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise.
+int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size,
+                              const int b_data[]);
+
+// Create a copy of an array passed as `src`.
+// You are expected to free memory with TfLiteIntArrayFree
+TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src);
+
+// Free memory of array `a`.
+void TfLiteIntArrayFree(TfLiteIntArray* a);
+
+// Fixed size list of floats. Used for per-channel quantization.
+typedef struct {
+  int size;
+// gcc 6.1+ have a bug where flexible members aren't properly handled
+// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
+#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
+    __GNUC_MINOR__ >= 1
+  float data[0];
+#else
+  float data[];
+#endif
+} TfLiteFloatArray;
+
+// Given the size (number of elements) in a TfLiteFloatArray, calculate its size
+// in bytes.
+int TfLiteFloatArrayGetSizeInBytes(int size);
+
+// Create a array of a given `size` (uninitialized entries).
+// This returns a pointer, that you must free using TfLiteFloatArrayFree().
+TfLiteFloatArray* TfLiteFloatArrayCreate(int size);
+
+// Free memory of array `a`.
+void TfLiteFloatArrayFree(TfLiteFloatArray* a);
+
+// Since we must not depend on any libraries, define a minimal subset of
+// error macros while avoiding names that have pre-conceived meanings like
+// assert and check.
+
+// Check whether value is true, and if not return kTfLiteError from
+// the current function (and report the error string msg).
+#define TF_LITE_ENSURE_MSG(context, value, msg)            \
+  do {                                                     \
+    if (!(value)) {                                        \
+      (context)->ReportError((context), __FILE__ " " msg); \
+      return kTfLiteError;                                 \
+    }                                                      \
+  } while (0)
+
+// Check whether the value `a` is true, and if not return kTfLiteError from
+// the current function, while also reporting the location of the error.
+#define TF_LITE_ENSURE(context, a)                                          \
+  do {                                                                      \
+    if (!(a)) {                                                             \
+      (context)->ReportError((context), "%s:%d %s was not true.", __FILE__, \
+                             __LINE__, #a);                                 \
+      return kTfLiteError;                                                  \
+    }                                                                       \
+  } while (0)
+
+#define TF_LITE_ENSURE_STATUS(a) \
+  do {                           \
+    if ((a) != kTfLiteOk) {      \
+      return kTfLiteError;       \
+    }                            \
+  } while (0)
+
+// Check whether the value `a == b` is true, and if not return kTfLiteError from
+// the current function, while also reporting the location of the error.
+// `a` and `b` may be evaluated more than once, so no side effects or
+// extremely expensive computations should be done.
+#define TF_LITE_ENSURE_EQ(context, a, b)                                       \
+  do {                                                                         \
+    if ((a) != (b)) {                                                          \
+      (context)->ReportError((context), "%s:%d %s != %s (%d != %d)", __FILE__, \
+                             __LINE__, #a, #b, (a), (b));                      \
+      return kTfLiteError;                                                     \
+    }                                                                          \
+  } while (0)
+
+#define TF_LITE_ENSURE_TYPES_EQ(context, a, b)                                 \
+  do {                                                                         \
+    if ((a) != (b)) {                                                          \
+      (context)->ReportError((context), "%s:%d %s != %s (%s != %s)", __FILE__, \
+                             __LINE__, #a, #b, TfLiteTypeGetName(a),           \
+                             TfLiteTypeGetName(b));                            \
+      return kTfLiteError;                                                     \
+    }                                                                          \
+  } while (0)
+
+#define TF_LITE_ENSURE_OK(context, status) \
+  do {                                     \
+    if ((status) != kTfLiteOk) {           \
+      return kTfLiteError;                 \
+    }                                      \
+  } while (0)
+
+// Single-precision complex data type compatible with the C99 definition.
+typedef struct {
+  float re, im;  // real and imaginary parts, respectively.
+} TfLiteComplex64;
+
+// Half precision data type compatible with the C99 definition.
+typedef struct {
+  uint16_t data;
+} TfLiteFloat16;
+
+// Types supported by tensor
+typedef enum {
+  kTfLiteNoType = 0,
+  kTfLiteFloat32 = 1,
+  kTfLiteInt32 = 2,
+  kTfLiteUInt8 = 3,
+  kTfLiteInt64 = 4,
+  kTfLiteString = 5,
+  kTfLiteBool = 6,
+  kTfLiteInt16 = 7,
+  kTfLiteComplex64 = 8,
+  kTfLiteInt8 = 9,
+  kTfLiteFloat16 = 10,
+} TfLiteType;
+
+// Return the name of a given type, for error reporting purposes.
+const char* TfLiteTypeGetName(TfLiteType type);
+
+// SupportedQuantizationTypes.
+typedef enum {
+  // No quantization.
+  kTfLiteNoQuantization = 0,
+  // Affine quantization (with support for per-channel quantization).
+  // Corresponds to TfLiteAffineQuantization.
+  kTfLiteAffineQuantization = 1,
+} TfLiteQuantizationType;
+
+// Structure specifying the quantization used by the tensor, if-any.
+typedef struct {
+  // The type of quantization held by params.
+  TfLiteQuantizationType type;
+  // Holds a reference to one of the quantization param structures specified
+  // below.
+  void* params;
+} TfLiteQuantization;
+
+// Legacy. Will be deprecated in favor of TfLiteAffineQuantization.
+// If per-layer quantization is specified this field will still be populated in
+// addition to TfLiteAffineQuantization.
+// Parameters for asymmetric quantization. Quantized values can be converted
+// back to float using:
+//     real_value = scale * (quantized_value - zero_point)
+typedef struct {
+  float scale;
+  int32_t zero_point;
+} TfLiteQuantizationParams;
+
+// Parameters for asymmetric quantization across a dimension (i.e per output
+// channel quantization).
+// quantized_dimension specifies which dimension the scales and zero_points
+// correspond to.
+// For a particular value in quantized_dimension, quantized values can be
+// converted back to float using:
+//     real_value = scale * (quantized_value - zero_point)
+typedef struct {
+  TfLiteFloatArray* scale;
+  TfLiteIntArray* zero_point;
+  int32_t quantized_dimension;
+} TfLiteAffineQuantization;
+
+/* A union of pointers that points to memory for a given tensor. */
+typedef union {
+  /* Do not access these members directly, if possible, use
+   * GetTensorData<TYPE>(tensor) instead, otherwise only access .data, as other
+   * members are deprecated. */
+  int32_t* i32;
+  int64_t* i64;
+  float* f;
+  TfLiteFloat16* f16;
+  char* raw;
+  const char* raw_const;
+  uint8_t* uint8;
+  bool* b;
+  int16_t* i16;
+  TfLiteComplex64* c64;
+  int8_t* int8;
+  /* Only use this member. */
+  void* data;
+} TfLitePtrUnion;
+
+// Memory allocation strategies. kTfLiteMmapRo is for read-only memory-mapped
+// data (or data externally allocated). kTfLiteArenaRw is arena allocated
+// data. kTfLiteDynamic is for tensors that are allocated during evaluation.
+typedef enum {
+  kTfLiteMemNone = 0,
+  kTfLiteMmapRo,
+  kTfLiteArenaRw,
+  kTfLiteArenaRwPersistent,
+  kTfLiteDynamic,
+} TfLiteAllocationType;
+
+// The delegates should use zero or positive integers to represent handles.
+// -1 is reserved from unallocated status.
+typedef int TfLiteBufferHandle;
+enum {
+  kTfLiteNullBufferHandle = -1,
+};
+
+// An tensor in the interpreter system which is a wrapper around a buffer of
+// data including a dimensionality (or NULL if not currently defined).
+typedef struct {
+  // The data type specification for data stored in `data`. This affects
+  // what member of `data` union should be used.
+  TfLiteType type;
+  // A union of data pointers. The appropriate type should be used for a typed
+  // tensor based on `type`.
+  TfLitePtrUnion data;
+  // A pointer to a structure representing the dimensionality interpretation
+  // that the buffer should have. NOTE: the product of elements of `dims`
+  // and the element datatype size should be equal to `bytes` below.
+  TfLiteIntArray* dims;
+  // Quantization information.
+  TfLiteQuantizationParams params;
+  // How memory is mapped
+  //  kTfLiteMmapRo: Memory mapped read only.
+  //  i.e. weights
+  //  kTfLiteArenaRw: Arena allocated read write memory
+  //  (i.e. temporaries, outputs).
+  TfLiteAllocationType allocation_type;
+  // The number of bytes required to store the data of this Tensor. I.e.
+  // (bytes of each element) * dims[0] * ... * dims[n-1].  For example, if
+  // type is kTfLiteFloat32 and dims = {3, 2} then
+  // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
+  size_t bytes;
+
+  // An opaque pointer to a tflite::MMapAllocation
+  const void* allocation;
+
+  // Null-terminated name of this tensor.
+  const char* name;
+
+  // The delegate which knows how to handle `buffer_handle`.
+  // WARNING: This is an experimental interface that is subject to change.
+  struct TfLiteDelegate* delegate;
+
+  // An integer buffer handle that can be handled by `delegate`.
+  // The value is valid only when delegate is not null.
+  // WARNING: This is an experimental interface that is subject to change.
+  TfLiteBufferHandle buffer_handle;
+
+  // If the delegate uses its own buffer (e.g. GPU memory), the delegate is
+  // responsible to set data_is_stale to true.
+  // `delegate->CopyFromBufferHandle` can be called to copy the data from
+  // delegate buffer.
+  // WARNING: This is an // experimental interface that is subject to change.
+  bool data_is_stale;
+
+  // True if the tensor is a variable.
+  bool is_variable;
+
+  // Quantization information. Replaces params field above.
+  TfLiteQuantization quantization;
+} TfLiteTensor;
+
+// Free data memory of tensor `t`.
+void TfLiteTensorDataFree(TfLiteTensor* t);
+
+// Free quantization data.
+void TfLiteQuantizationFree(TfLiteQuantization* quantization);
+
+// Free memory of tensor `t`.
+void TfLiteTensorFree(TfLiteTensor* t);
+
+// Set all of a tensor's fields (and free any previously allocated data).
+void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims,
+                       TfLiteQuantizationParams quantization, char* buffer,
+                       size_t size, TfLiteAllocationType allocation_type,
+                       const void* allocation, bool is_variable,
+                       TfLiteTensor* tensor);
+
+// Resize the allocated data of a (dynamic) tensor. Tensors with allocation
+// types other than kTfLiteDynamic will be ignored.
+void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor);
+
+// A structure representing an instance of a node.
+// This structure only exhibits the inputs, outputs and user defined data, not
+// other features like the type.
+typedef struct {
+  // Inputs to this node expressed as indices into the simulator's tensors.
+  TfLiteIntArray* inputs;
+
+  // Outputs to this node expressed as indices into the simulator's tensors.
+  TfLiteIntArray* outputs;
+
+  // intermediate tensors to this node expressed as indices into the simulator's
+  // tensors.
+  TfLiteIntArray* intermediates;
+
+  // Temporary tensors uses during the computations. This usually contains no
+  // tensors, but ops are allowed to change that if they need scratch space of
+  // any sort.
+  TfLiteIntArray* temporaries;
+
+  // Opaque data provided by the node implementer through `Registration.init`.
+  void* user_data;
+
+  // Opaque data provided to the node if the node is a builtin. This is usually
+  // a structure defined in builtin_op_data.h
+  void* builtin_data;
+
+  // Custom initial data. This is the opaque data provided in the flatbuffer.
+  // WARNING: This is an experimental interface that is subject to change.
+  const void* custom_initial_data;
+  int custom_initial_data_size;
+
+  // The pointer to the delegate. This is non-null only when the node is
+  // created by calling `interpreter.ModifyGraphWithDelegate`.
+  // WARNING: This is an experimental interface that is subject to change.
+  struct TfLiteDelegate* delegate;
+} TfLiteNode;
+
+typedef struct TfLiteContext {
+  // Number of tensors in the context.
+  size_t tensors_size;
+
+  // The execution plan contains a list of the node indices in execution
+  // order. execution_plan->size is the current number of nodes. And,
+  // execution_plan->data[0] is the first node that needs to be run.
+  // TfLiteDelegates can traverse the current execution plan by iterating
+  // through each member of this array and using GetNodeAndRegistration() to
+  // access details about a node. i.e.
+  // TfLiteIntArray* execution_plan;
+  // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan));
+  // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) {
+  //    int node_index = execution_plan->data[exec_index];
+  //    TfLiteNode* node;
+  //    TfLiteRegistration* reg;
+  //    context->GetNodeAndRegistration(context, node_index, &node, &reg);
+  // }
+  // WARNING: This is an experimental interface that is subject to change.
+  TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context,
+                                   TfLiteIntArray** execution_plan);
+
+  // An array of tensors in the interpreter context (of length `tensors_size`)
+  TfLiteTensor* tensors;
+
+  // opaque full context ptr (an opaque c++ data structure)
+  void* impl_;
+
+  // Request memory pointer be resized. Updates dimensions on the tensor.
+  // NOTE: ResizeTensor takes ownership of newSize.
+  TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor,
+                               TfLiteIntArray* new_size);
+  // Request that an error be reported with format string msg.
+  void (*ReportError)(struct TfLiteContext*, const char* msg, ...);
+
+  // Add `tensors_to_add` tensors, preserving pre-existing Tensor entries.  If
+  // non-null, the value pointed to by `first_new_tensor_index` will be set to
+  // the index of the first new tensor.
+  TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add,
+                             int* first_new_tensor_index);
+
+  // Get a Tensor node by node_index.
+  // WARNING: This is an experimental interface that is subject to change.
+  TfLiteStatus (*GetNodeAndRegistration)(
+      struct TfLiteContext*, int node_index, TfLiteNode** node,
+      struct TfLiteRegistration** registration);
+
+  // Replace ops with one or more stub delegate operations. This function
+  // does not take ownership of `nodes_to_replace`.
+  TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)(
+      struct TfLiteContext*, struct TfLiteRegistration registration,
+      const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate);
+
+  // Number of threads that are recommended to subsystems like gemmlowp and
+  // eigen.
+  int recommended_num_threads;
+
+  // Access external contexts by type.
+  // WARNING: This is an experimental interface that is subject to change.
+  TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*,
+                                               TfLiteExternalContextType);
+  // Set the value of a external context. Does not take ownership of the
+  // pointer.
+  // WARNING: This is an experimental interface that is subject to change.
+  void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType,
+                             TfLiteExternalContext*);
+
+  // Flag for allowing float16 precision for FP32 calculation.
+  // default: false.
+  // WARNING: This is an experimental API and subject to change.
+  bool allow_fp32_relax_to_fp16;
+
+  // Pointer to the op-level profiler, if set; nullptr otherwise.
+  void* profiler;
+} TfLiteContext;
+
+typedef struct TfLiteRegistration {
+  // Initializes the op from serialized data.
+  // If a built-in op:
+  //   `buffer` is the op's params data (TfLiteLSTMParams*).
+  //   `length` is zero.
+  // If custom op:
+  //   `buffer` is the op's `custom_options`.
+  //   `length` is the size of the buffer.
+  //
+  // Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer
+  // or an instance of a struct).
+  //
+  // The returned pointer will be stored with the node in the `user_data` field,
+  // accessible within prepare and invoke functions below.
+  // NOTE: if the data is already in the desired format, simply implement this
+  // function to return `nullptr` and implement the free function to be a no-op.
+  void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
+
+  // The pointer `buffer` is the data previously returned by an init invocation.
+  void (*free)(TfLiteContext* context, void* buffer);
+
+  // prepare is called when the inputs this node depends on have been resized.
+  // context->ResizeTensor() can be called to request output tensors to be
+  // resized.
+  //
+  // Returns kTfLiteOk on success.
+  TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
+
+  // Execute the node (should read node->inputs and output to node->outputs).
+  // Returns kTfLiteOk on success.
+  TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
+
+  // profiling_string is called during summarization of profiling information
+  // in order to group executions together. Providing a value here will cause a
+  // given op to appear multiple times is the profiling report. This is
+  // particularly useful for custom ops that can perform significantly
+  // different calculations depending on their `user-data`.
+  const char* (*profiling_string)(const TfLiteContext* context,
+                                  const TfLiteNode* node);
+
+  // Builtin codes. If this kernel refers to a builtin this is the code
+  // of the builtin. This is so we can do marshaling to other frameworks like
+  // NN API.
+  // Note: It is the responsibility of the registration binder to set this
+  // properly.
+  int32_t builtin_code;
+
+  // Custom op name. If the op is a builtin, this will be null.
+  // Note: It is the responsibility of the registration binder to set this
+  // properly.
+  // WARNING: This is an experimental interface that is subject to change.
+  const char* custom_name;
+
+  // The version of the op.
+  // Note: It is the responsibility of the registration binder to set this
+  // properly.
+  int version;
+} TfLiteRegistration;
+
+// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
+// values should be 1, 2, 4, 8, ...etc.
+typedef enum {
+  kTfLiteDelegateFlagsNone = 0,
+  // The flag is set if the delegate can handle dynamic sized tensors.
+  // For example, the output shape of a `Resize` op with non-constant shape
+  // can only be inferred when the op is invoked.
+  // In this case, the Delegate is responsible for calling
+  // `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling
+  // `ResizeTensor` when invoking the op.
+  //
+  // If the delegate isn't capable to handle dynamic tensors, this flag need
+  // to be set to false.
+  kTfLiteDelegateFlagsAllowDynamicTensors = 1
+} TfLiteDelegateFlags;
+
+// WARNING: This is an experimental interface that is subject to change.
+typedef struct TfLiteDelegate {
+  // Data that delegate needs to identify itself. This data is owned by the
+  // delegate. The delegate is owned in the user code, so the delegate is
+  // responsible for doing this when it is destroyed.
+  void* data_;
+
+  // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
+  // delegate a view of the current graph through TfLiteContext*. It typically
+  // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
+  // to ask the TensorFlow lite runtime to create macro-nodes to represent
+  // delegated subgraphs of the original graph.
+  TfLiteStatus (*Prepare)(TfLiteContext* context,
+                          struct TfLiteDelegate* delegate);
+
+  // Copy the data from delegate buffer handle into raw memory of the given
+  // 'tensor'. This cannot be null. The delegate is allowed to allocate the raw
+  // bytes as long as it follows the rules for kTfLiteDynamic tensors.
+  TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context,
+                                       struct TfLiteDelegate* delegate,
+                                       TfLiteBufferHandle buffer_handle,
+                                       TfLiteTensor* tensor);
+
+  // Copy the data from raw memory of the given 'tensor' to delegate buffer
+  // handle. This can be null if the delegate doesn't use its own buffer.
+  TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context,
+                                     struct TfLiteDelegate* delegate,
+                                     TfLiteBufferHandle buffer_handle,
+                                     TfLiteTensor* tensor);
+
+  // Free the Delegate Buffer Handle. Note: This only frees the handle, but
+  // this doesn't release the underlying resource (e.g. textures). The
+  // resources are either owned by application layer or the delegate.
+  // This can be null if the delegate doesn't use its own buffer.
+  void (*FreeBufferHandle)(TfLiteContext* context,
+                           struct TfLiteDelegate* delegate,
+                           TfLiteBufferHandle* handle);
+
+  // Bitmask flags. See the comments in `TfLiteDelegateFlags`.
+  int64_t flags;
+} TfLiteDelegate;
+
+// Build a 'null' delegate, with all the fields properly set to their default
+// values.
+TfLiteDelegate TfLiteDelegateCreate();
+
+// WARNING: This is an experimental interface that is subject to change.
+//
+// Currently, TfLiteDelegateParams has to be allocated in a way that it's
+// trivially destructable. It will be stored as `builtin_data` field in
+// `TfLiteNode` of the delegate node.
+//
+// See also the `CreateDelegateParams` function in `interpreter.cc` details.
+typedef struct {
+  TfLiteDelegate* delegate;
+  TfLiteIntArray* nodes_to_replace;
+  TfLiteIntArray* input_tensors;
+  TfLiteIntArray* output_tensors;
+} TfLiteDelegateParams;
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif  // __cplusplus
+#endif  // TENSORFLOW_LITE_C_C_API_INTERNAL_H_
diff --git a/tensorflow/lite/tools/benchmark/experimental/ios/BUILD.apple b/tensorflow/lite/tools/benchmark/experimental/ios/BUILD.apple
new file mode 100644
index 0000000..9b4f835
--- /dev/null
+++ b/tensorflow/lite/tools/benchmark/experimental/ios/BUILD.apple
@@ -0,0 +1,22 @@
+load("//tensorflow/lite/experimental/ios:ios.bzl", "TFL_MINIMUM_OS_VERSION")
+load("@build_bazel_rules_apple//apple:ios.bzl", "ios_static_framework")
+
+package(
+    licenses = ["notice"],  # Apache 2.0
+)
+
+# Main target for the benchmark tool iOS framework.
+# bazel build --config=ios_fat -c opt //tensorflow/lite/tools/benchmark/experimental/ios:TensorFlowLiteBenchmarkC_framework
+ios_static_framework(
+    name = "TensorFlowLiteBenchmarkC_framework",
+    hdrs = [
+        "//tensorflow/lite/tools/benchmark:logging.h",
+        "//tensorflow/lite/tools/benchmark/experimental/c:benchmark_c_api.h",
+        "//tensorflow/lite/tools/benchmark/experimental/c:c_api_types.h",
+    ],
+    bundle_name = "TensorFlowLiteBenchmarkC",
+    minimum_os_version = TFL_MINIMUM_OS_VERSION,
+    deps = [
+        "//tensorflow/lite/tools/benchmark/experimental/c:benchmark_c_api",
+    ],
+)