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diff --git a/inference-engine/samples/classification_sample_async/main.cpp b/inference-engine/samples/classification_sample_async/main.cpp
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+/*
+// Copyright (c) 2018 Intel Corporation
+//
+// 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.
+*/
+
+/**
+* @brief The entry point the Inference Engine sample application
+* @file classification_sample/main.cpp
+* @example classification_sample/main.cpp
+*/
+
+#include <fstream>
+#include <iomanip>
+#include <vector>
+#include <chrono>
+#include <memory>
+#include <string>
+#include <map>
+
+#include <inference_engine.hpp>
+
+#include <format_reader/format_reader_ptr.h>
+
+#include <samples/common.hpp>
+#include <samples/slog.hpp>
+#include <samples/args_helper.hpp>
+
+#include <sys/stat.h>
+#include <ext_list.hpp>
+
+#include "classification_sample_async.h"
+
+using namespace InferenceEngine;
+
+bool ParseAndCheckCommandLine(int argc, char *argv[]) {
+    // ---------------------------Parsing and validation of input args--------------------------------------
+    slog::info << "Parsing input parameters" << slog::endl;
+
+    gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
+    if (FLAGS_h) {
+        showUsage();
+        return false;
+    }
+    slog::info << "Parsing input parameters" << slog::endl;
+
+    if (FLAGS_ni < 1) {
+        throw std::logic_error("Parameter -ni must be more than 0 ! (default 1)");
+    }
+
+    if (FLAGS_nireq < 1) {
+        throw std::logic_error("Parameter -nireq must be more than 0 ! (default 1)");
+    }
+
+    if (FLAGS_i.empty()) {
+        throw std::logic_error("Parameter -i is not set");
+    }
+
+    if (FLAGS_m.empty()) {
+        throw std::logic_error("Parameter -m is not set");
+    }
+
+    if (FLAGS_ni < FLAGS_nireq) {
+        throw std::logic_error("Number of iterations could not be less than requests quantity");
+    }
+
+    return true;
+}
+
+int main(int argc, char *argv[]) {
+    try {
+        slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << slog::endl;
+
+        // ------------------------------ Parsing and validation of input args ---------------------------------
+        if (!ParseAndCheckCommandLine(argc, argv)) {
+            return 0;
+        }
+
+        /** This vector stores paths to the processed images **/
+        std::vector<std::string> imageNames;
+        parseImagesArguments(imageNames);
+        if (imageNames.empty()) throw std::logic_error("No suitable images were found");
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 1. Load Plugin for inference engine -------------------------------------
+        slog::info << "Loading plugin" << slog::endl;
+        InferencePlugin plugin = PluginDispatcher({ FLAGS_pp, "../../../lib/intel64" , "" }).getPluginByDevice(FLAGS_d);
+
+        /** Loading default extensions **/
+        if (FLAGS_d.find("CPU") != std::string::npos) {
+            /**
+             * cpu_extensions library is compiled from "extension" folder containing
+             * custom MKLDNNPlugin layer implementations. These layers are not supported
+             * by mkldnn, but they can be useful for inferring custom topologies.
+            **/
+            plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
+        }
+
+        if (!FLAGS_l.empty()) {
+            // CPU(MKLDNN) extensions are loaded as a shared library and passed as a pointer to base extension
+            IExtensionPtr extension_ptr = make_so_pointer<IExtension>(FLAGS_l);
+            plugin.AddExtension(extension_ptr);
+            slog::info << "CPU Extension loaded: " << FLAGS_l << slog::endl;
+        }
+        if (!FLAGS_c.empty()) {
+            // clDNN Extensions are loaded from an .xml description and OpenCL kernel files
+            plugin.SetConfig({{PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c}});
+            slog::info << "GPU Extension loaded: " << FLAGS_c << slog::endl;
+        }
+
+        ResponseDesc resp;
+        /** Printing plugin version **/
+        printPluginVersion(plugin, std::cout);
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
+        slog::info << "Loading network files" << slog::endl;
+
+        CNNNetReader networkReader;
+        /** Read network model **/
+        networkReader.ReadNetwork(FLAGS_m);
+
+        /** Extract model name and load weights **/
+        std::string binFileName = fileNameNoExt(FLAGS_m) + ".bin";
+        networkReader.ReadWeights(binFileName);
+
+        CNNNetwork network = networkReader.getNetwork();
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 3. Configure input & output ---------------------------------------------
+
+        // --------------------------- Prepare input blobs -----------------------------------------------------
+        slog::info << "Preparing input blobs" << slog::endl;
+
+        /** Taking information about all topology inputs **/
+        InputsDataMap inputInfo(network.getInputsInfo());
+        if (inputInfo.size() != 1) throw std::logic_error("Sample supports topologies only with 1 input");
+
+        auto inputInfoItem = *inputInfo.begin();
+
+        /** Specifying the precision and layout of input data provided by the user.
+         * This should be called before load of the network to the plugin **/
+        inputInfoItem.second->setPrecision(Precision::U8);
+        inputInfoItem.second->setLayout(Layout::NCHW);
+
+        std::vector<std::shared_ptr<unsigned char>> imagesData;
+        for (auto & i : imageNames) {
+            FormatReader::ReaderPtr reader(i.c_str());
+            if (reader.get() == nullptr) {
+                slog::warn << "Image " + i + " cannot be read!" << slog::endl;
+                continue;
+            }
+            /** Store image data **/
+            std::shared_ptr<unsigned char> data(
+                    reader->getData(inputInfoItem.second->getTensorDesc().getDims()[3],
+                                    inputInfoItem.second->getTensorDesc().getDims()[2]));
+            if (data.get() != nullptr) {
+                imagesData.push_back(data);
+            }
+        }
+        if (imagesData.empty()) throw std::logic_error("Valid input images were not found!");
+
+        /** Setting batch size using image count **/
+        network.setBatchSize(imagesData.size());
+        size_t batchSize = network.getBatchSize();
+        slog::info << "Batch size is " << std::to_string(batchSize) << slog::endl;
+
+        // ------------------------------ Prepare output blobs -------------------------------------------------
+        slog::info << "Preparing output blobs" << slog::endl;
+
+        OutputsDataMap outputInfo(network.getOutputsInfo());
+        std::vector <Blob::Ptr> outputBlobs;
+        for (size_t i = 0; i < FLAGS_nireq; i++) {
+            auto outputBlob = make_shared_blob<PrecisionTrait<Precision::FP32>::value_type>(outputInfo.begin()->second->getTensorDesc());
+            outputBlob->allocate();
+            outputBlobs.push_back(outputBlob);
+        }
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 4. Loading model to the plugin ------------------------------------------
+        slog::info << "Loading model to the plugin" << slog::endl;
+
+        std::map<std::string, std::string> config;
+        if (FLAGS_pc) {
+            config[PluginConfigParams::KEY_PERF_COUNT] = PluginConfigParams::YES;
+        }
+
+        ExecutableNetwork executable_network = plugin.LoadNetwork(network, {});
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 5. Create infer request -------------------------------------------------
+        std::vector<InferRequest> inferRequests;
+        for (size_t i = 0; i < FLAGS_nireq; i++) {
+            InferRequest inferRequest = executable_network.CreateInferRequest();
+            inferRequests.push_back(inferRequest);
+        }
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 6. Prepare input --------------------------------------------------------
+        BlobMap inputBlobs;
+        for (auto & item : inputInfo) {
+            auto input = make_shared_blob<PrecisionTrait<Precision::U8>::value_type>(item.second->getTensorDesc());
+            input->allocate();
+            inputBlobs[item.first] = input;
+
+            auto dims = input->getTensorDesc().getDims();
+            /** Fill input tensor with images. First b channel, then g and r channels **/
+            size_t num_channels = dims[1];
+            size_t image_size = dims[3] * dims[2];
+
+            /** Iterate over all input images **/
+            for (size_t image_id = 0; image_id < imagesData.size(); ++image_id) {
+                /** Iterate over all pixel in image (b,g,r) **/
+                for (size_t pid = 0; pid < image_size; pid++) {
+                    /** Iterate over all channels **/
+                    for (size_t ch = 0; ch < num_channels; ++ch) {
+                        /**          [images stride + channels stride + pixel id ] all in bytes            **/
+                        input->data()[image_id * image_size * num_channels + ch * image_size + pid] = imagesData.at(image_id).get()[pid*num_channels + ch];
+                    }
+                }
+            }
+        }
+
+        for (size_t i = 0; i < FLAGS_nireq; i++) {
+            inferRequests[i].SetBlob(inputBlobs.begin()->first, inputBlobs.begin()->second);
+            inferRequests[i].SetBlob(outputInfo.begin()->first, outputBlobs[i]);
+        }
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 7. Do inference ---------------------------------------------------------
+        slog::info << "Start inference (" << FLAGS_ni << " iterations)" << slog::endl;
+
+        typedef std::chrono::high_resolution_clock Time;
+        typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
+        typedef std::chrono::duration<float> fsec;
+
+        double total = 0.0;
+        /** Start inference & calc performance **/
+        auto t0 = Time::now();
+
+        size_t currentInfer = 0;
+        size_t prevInfer = (FLAGS_nireq > 1) ? 1 : 0;
+
+
+        // warming up
+        inferRequests[0].StartAsync();
+        inferRequests[0].Wait(10000);
+
+        for (int iter = 0; iter < FLAGS_ni + FLAGS_nireq; ++iter) {
+            if (iter < FLAGS_ni) {
+                inferRequests[currentInfer].StartAsync();
+            }
+            inferRequests[prevInfer].Wait(10000);
+
+            currentInfer++;
+            if (currentInfer >= FLAGS_nireq) {
+                currentInfer = 0;
+            }
+            prevInfer++;
+            if (prevInfer >= FLAGS_nireq) {
+                prevInfer = 0;
+            }
+        }
+        auto t1 = Time::now();
+        fsec fs = t1 - t0;
+        ms d = std::chrono::duration_cast<ms>(fs);
+        total = d.count();
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 8. Process output -------------------------------------------------------
+        slog::info << "Processing output blobs" << slog::endl;
+
+        for (size_t i = 0; i < FLAGS_nireq; i++) {
+            /** Validating -nt value **/
+            const int resultsCnt = outputBlobs[i]->size() / batchSize;
+            if (FLAGS_nt > resultsCnt || FLAGS_nt < 1) {
+                slog::warn << "-nt " << FLAGS_nt << " is not available for this network (-nt should be less than " \
+                          << resultsCnt+1 << " and more than 0)\n            will be used maximal value : " << resultsCnt << slog::endl;
+                FLAGS_nt = resultsCnt;
+            }
+            /** This vector stores id's of top N results **/
+            std::vector<unsigned> results;
+            TopResults(FLAGS_nt, *outputBlobs[i], results);
+
+            std::cout << std::endl << "Top " << FLAGS_nt << " results:" << std::endl << std::endl;
+
+            /** Read labels from file (e.x. AlexNet.labels) **/
+            bool labelsEnabled = false;
+            std::string labelFileName = fileNameNoExt(FLAGS_m) + ".labels";
+            std::vector<std::string> labels;
+
+            std::ifstream inputFile;
+            inputFile.open(labelFileName, std::ios::in);
+            if (inputFile.is_open()) {
+                std::string strLine;
+                while (std::getline(inputFile, strLine)) {
+                    trim(strLine);
+                    labels.push_back(strLine);
+                }
+                labelsEnabled = true;
+            }
+
+            /** Print the result iterating over each batch **/
+            for (int image_id = 0; image_id < batchSize; ++image_id) {
+                std::cout << "Image " << imageNames[image_id] << std::endl << std::endl;
+                for (size_t id = image_id * FLAGS_nt, cnt = 0; cnt < FLAGS_nt; ++cnt, ++id) {
+                    std::cout.precision(7);
+                    /** Getting probability for resulting class **/
+                    auto result = outputBlobs[i]->buffer().
+                            as<PrecisionTrait<Precision::FP32>::value_type*>()[results[id] + image_id*(outputBlobs[i]->size() / batchSize)];
+                    std::cout << std::left << std::fixed << results[id] << " " << result;
+                    if (labelsEnabled) {
+                        std::cout << " label " << labels[results[id]] << std::endl;
+                    } else {
+                        std::cout << " label #" << results[id] << std::endl;
+                    }
+                }
+                std::cout << std::endl;
+            }
+        }
+        // -----------------------------------------------------------------------------------------------------
+        std::cout << std::endl << "total inference time: " << total << std::endl;
+        std::cout << std::endl << "Throughput: " << 1000 * static_cast<double>(FLAGS_ni) * batchSize / total << " FPS" << std::endl;
+        std::cout << std::endl;
+
+        /** Show performance results **/
+        std::map<std::string, InferenceEngineProfileInfo> performanceMap;
+        if (FLAGS_pc) {
+            for (size_t nireq = 0; nireq < FLAGS_nireq; nireq++) {
+                performanceMap = inferRequests[nireq].GetPerformanceCounts();
+                printPerformanceCounts(performanceMap, std::cout);
+            }
+        }
+    }
+    catch (const std::exception& error) {
+        slog::err << error.what() << slog::endl;
+        return 1;
+    }
+    catch (...) {
+        slog::err << "Unknown/internal exception happened." << slog::endl;
+        return 1;
+    }
+
+    slog::info << "Execution successful" << slog::endl;
+    return 0;
+}