diff --git a/tests/python/nightly/quantization/logs/rasp4b.mobilenetv2_1.0.f32.log.backup b/tests/python/nightly/quantization/logs/rasp4b.mobilenetv2_1.0.f32.log.backup
new file mode 100644
index 000000000000..a42135e707c4
--- /dev/null
+++ b/tests/python/nightly/quantization/logs/rasp4b.mobilenetv2_1.0.f32.log.backup
@@ -0,0 +1,64 @@
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diff --git a/tests/python/nightly/quantization/logs/rasp4b.mobilenetv2_1.0.i8.i32.log.backup b/tests/python/nightly/quantization/logs/rasp4b.mobilenetv2_1.0.i8.i32.log.backup
new file mode 100644
index 000000000000..d5f4824a34c3
--- /dev/null
+++ b/tests/python/nightly/quantization/logs/rasp4b.mobilenetv2_1.0.i8.i32.log.backup
@@ -0,0 +1,64 @@
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diff --git a/tests/python/nightly/quantization/logs/rasp4b.resnet18_v1.f32.log.backup b/tests/python/nightly/quantization/logs/rasp4b.resnet18_v1.f32.log.backup
new file mode 100644
index 000000000000..4f7743b75850
--- /dev/null
+++ b/tests/python/nightly/quantization/logs/rasp4b.resnet18_v1.f32.log.backup
@@ -0,0 +1,26 @@
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diff --git a/tests/python/nightly/quantization/logs/rasp4b.resnet18_v1.i8.i32.log.backup b/tests/python/nightly/quantization/logs/rasp4b.resnet18_v1.i8.i32.log.backup
new file mode 100644
index 000000000000..aa88bbe4f1e9
--- /dev/null
+++ b/tests/python/nightly/quantization/logs/rasp4b.resnet18_v1.i8.i32.log.backup
@@ -0,0 +1,22 @@
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+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_nchw_spatial_pack.arm_cpu", [["TENSOR", [1, 128, 28, 28], "int8"], ["TENSOR", [128, 128, 3, 3], "int8"], [1, 1], [1, 1, 1, 1], [1, 1], "int32"], {}], "config": {"index": 30733, "code_hash": null, "entity": [["tile_co", "sp", [-1, 32]], ["tile_oh", "sp", [-1, 2]], ["tile_ow", "sp", [-1, 14]], ["reorder_0", "re", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ["ann_reduce", "an", ["none", "unroll"]], ["ann_spatial", "an", ["unroll", "unroll", "vec"]]]}, "result": [[0.0078687094], 0, 1.736840009689331, 1604208749.0489216], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_NCHWc.x86", [["TENSOR", [1, 128, 28, 28], "int8"], ["TENSOR", [256, 128, 1, 1], "int8"], [2, 2], [0, 0, 0, 0], [1, 1], "NCHW", "NCHW", "int32"], {}], "config": {"index": 130, "code_hash": null, "entity": [["tile_ic", "sp", [-1, 4]], ["tile_oc", "sp", [-1, 128]], ["tile_ow", "sp", [-1, 2]], ["tile_oh", "ot", 1]]}, "result": [[0.0005048412000000001], 0, 1.1250295639038086, 1604209355.977946], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_nchw_spatial_pack.arm_cpu", [["TENSOR", [1, 128, 28, 28], "int8"], ["TENSOR", [256, 128, 1, 1], "int8"], [2, 2], [0, 0, 0, 0], [1, 1], "int32"], {}], "config": {"index": 2632, "code_hash": null, "entity": [["tile_co", "sp", [-1, 16]], ["tile_oh", "sp", [-1, 1]], ["tile_ow", "sp", [-1, 2]], ["reorder_0", "re", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ["ann_reduce", "an", ["none", "unroll"]], ["ann_spatial", "an", ["none", "none", "vec"]]]}, "result": [[0.0005214222], 0, 0.9678590297698975, 1604209906.0243506], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_NCHWc.x86", [["TENSOR", [1, 256, 14, 14], "int8"], ["TENSOR", [256, 256, 3, 3], "int8"], [1, 1], [1, 1, 1, 1], [1, 1], "NCHW", "NCHW", "int32"], {}], "config": {"index": 302, "code_hash": null, "entity": [["tile_ic", "sp", [-1, 32]], ["tile_oc", "sp", [-1, 64]], ["tile_ow", "sp", [-1, 7]], ["unroll_kw", "ot", true]]}, "result": [[0.007499634600000001], 0, 1.2390851974487305, 1604210411.8254368], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_nchw_spatial_pack.arm_cpu", [["TENSOR", [1, 256, 14, 14], "int8"], ["TENSOR", [256, 256, 3, 3], "int8"], [1, 1], [1, 1, 1, 1], [1, 1], "int32"], {}], "config": {"index": 15530, "code_hash": null, "entity": [["tile_co", "sp", [-1, 32]], ["tile_oh", "sp", [-1, 2]], ["tile_ow", "sp", [-1, 14]], ["reorder_0", "re", [0, 1, 2, 3, 4, 5, 6, 9, 7, 8]], ["ann_reduce", "an", ["none", "unroll"]], ["ann_spatial", "an", ["unroll", "unroll", "vec"]]]}, "result": [[0.0076964978], 0, 1.7472355365753174, 1604210875.9447825], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_NCHWc.x86", [["TENSOR", [1, 256, 14, 14], "int8"], ["TENSOR", [512, 256, 1, 1], "int8"], [2, 2], [0, 0, 0, 0], [1, 1], "NCHW", "NCHW", "int32"], {}], "config": {"index": 244, "code_hash": null, "entity": [["tile_ic", "sp", [-1, 2]], ["tile_oc", "sp", [-1, 128]], ["tile_ow", "sp", [-1, 4]], ["tile_oh", "ot", 1]]}, "result": [[0.0004964132], 0, 1.0575084686279297, 1604211394.9509404], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_nchw_spatial_pack.arm_cpu", [["TENSOR", [1, 256, 14, 14], "int8"], ["TENSOR", [512, 256, 1, 1], "int8"], [2, 2], [0, 0, 0, 0], [1, 1], "int32"], {}], "config": {"index": 4165, "code_hash": null, "entity": [["tile_co", "sp", [-1, 32]], ["tile_oh", "sp", [-1, 1]], ["tile_ow", "sp", [-1, 1]], ["reorder_0", "re", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ["ann_reduce", "an", ["none", "none"]], ["ann_spatial", "an", ["unroll", "unroll", "vec"]]]}, "result": [[0.000537594], 0, 0.8399600982666016, 1604211795.8321984], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_NCHWc.x86", [["TENSOR", [1, 512, 7, 7], "int8"], ["TENSOR", [512, 512, 3, 3], "int8"], [1, 1], [1, 1, 1, 1], [1, 1], "NCHW", "NCHW", "int32"], {}], "config": {"index": 374, "code_hash": null, "entity": [["tile_ic", "sp", [-1, 16]], ["tile_oc", "sp", [-1, 128]], ["tile_ow", "sp", [-1, 7]], ["unroll_kw", "ot", true]]}, "result": [[0.0076132032], 0, 1.1933751106262207, 1604212242.5496144], "version": 0.2, "tvm_version": "0.7.dev1"}
+{"input": ["llvm -keys=arm_cpu,cpu -device=arm_cpu -mattr=+neon -model=bcm2711 -mtriple=arm-linux-gnueabihf", "conv2d_nchw_spatial_pack.arm_cpu", [["TENSOR", [1, 512, 7, 7], "int8"], ["TENSOR", [512, 512, 3, 3], "int8"], [1, 1], [1, 1, 1, 1], [1, 1], "int32"], {}], "config": {"index": 747, "code_hash": null, "entity": [["tile_co", "sp", [-1, 128]], ["tile_oh", "sp", [-1, 1]], ["tile_ow", "sp", [-1, 7]], ["reorder_0", "re", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ["ann_reduce", "an", ["none", "unroll"]], ["ann_spatial", "an", ["none", "none", "vec"]]]}, "result": [[0.0079320688], 0, 1.1609034538269043, 1604212700.94038], "version": 0.2, "tvm_version": "0.7.dev1"}
diff --git a/tests/python/nightly/quantization/tune_hago_arm.py b/tests/python/nightly/quantization/tune_hago_arm.py
new file mode 100644
index 000000000000..6ace4f093fe2
--- /dev/null
+++ b/tests/python/nightly/quantization/tune_hago_arm.py
@@ -0,0 +1,231 @@
+import os
+import argparse
+
+import numpy as np
+import mxnet as mx
+from mxnet import gluon
+import tvm
+from tvm import hago
+from tvm import relay
+from tvm import te
+from tvm import autotvm
+from tvm import relay
+import tvm.relay.testing
+from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
+from tvm.contrib.util import tempdir
+import tvm.contrib.graph_runtime as runtime
+from common_utils import *
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model", default="resnet18_v1", help="model to quantize")
+parser.add_argument("--skip_fp32", default=False, action='store_true')
+parser.add_argument("--run_all", default=False, action='store_true')
+args = parser.parse_args()
+
+
+target = tvm.target.arm_cpu("rasp4b")
+device_key = "rasp4b"
+use_android = False
+
+remote_host = "kraken"
+remote_port = 9191 
+
+log_dir = "logs/"
+batch_size = 1
+
+def get_val_data(img_size,
+                 rec_val,
+                 batch_size,
+                 num_workers=4):
+    rec_val = os.path.expanduser(rec_val)
+    mean_rgb = [123.68, 116.779, 103.939]
+    std_rgb = [58.393, 57.12, 57.375]
+    ctx = tvm.cpu()
+    def batch_fn(batch, ctx):
+        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+        return data, label
+
+    val_data = mx.io.ImageRecordIter(
+        path_imgrec         = rec_val,
+        preprocess_threads  = num_workers,
+        shuffle             = True,
+        seed                = 0,
+        batch_size          = batch_size,
+        resize              = 256,
+        data_shape          = (3, img_size, img_size),
+        mean_r              = mean_rgb[0],
+        mean_g              = mean_rgb[1],
+        mean_b              = mean_rgb[2],
+        std_r               = std_rgb[0],
+        std_g               = std_rgb[1],
+        std_b               = std_rgb[2],
+    )
+    return val_data, batch_fn
+
+
+def get_model(model_name):
+    gluon_model = gluon.model_zoo.vision.get_model(model_name, pretrained=True)
+    img_size = 299 if model_name == 'inceptionv3' else 224
+    data_shape = (batch_size, 3, img_size, img_size)
+    mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
+    return mod, params, data_shape
+
+
+# You can skip the implementation of this function for this tutorial.
+def tune_tasks(
+    tasks,
+    measure_option,
+    tuner="xgb",
+    n_trial=1000,
+    early_stopping=None,
+    log_filename="tuning.log",
+    use_transfer_learning=True,
+):
+    # create tmp log file
+    tmp_log_file = os.path.join(log_dir, log_filename + ".tmp")
+    if os.path.exists(tmp_log_file):
+        os.remove(tmp_log_file)
+
+    for i, tsk in enumerate(reversed(tasks)):
+        prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+        # create tuner
+        if tuner == "xgb" or tuner == "xgb-rank":
+            tuner_obj = XGBTuner(tsk, loss_type="rank")
+        elif tuner == "xgb_knob":
+            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+        elif tuner == "ga":
+            tuner_obj = GATuner(tsk, pop_size=50)
+        elif tuner == "random":
+            tuner_obj = RandomTuner(tsk)
+        elif tuner == "gridsearch":
+            tuner_obj = GridSearchTuner(tsk)
+        else:
+            raise ValueError("Invalid tuner: " + tuner)
+
+        if use_transfer_learning:
+            if os.path.isfile(tmp_log_file):
+                tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
+
+        # do tuning
+        tsk_trial = min(n_trial, len(tsk.config_space))
+        tuner_obj.tune(
+            n_trial=tsk_trial,
+            early_stopping=early_stopping,
+            measure_option=measure_option,
+            callbacks=[
+                autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
+                autotvm.callback.log_to_file(tmp_log_file),
+            ],
+        )
+
+    # pick best records to a cache file
+    log_file = os.path.join(log_dir, log_filename)
+    autotvm.record.pick_best(tmp_log_file, log_file)
+    os.remove(tmp_log_file)
+
+
+def tune_and_evaluate(mod, params, input_shape, tuning_opt, eval_only=False):
+    # extract workloads from relay program
+    print("Extract tasks...")
+    tasks = autotvm.task.extract_from_program(
+        mod, target=target, params=params, ops=(relay.op.get("nn.conv2d"),)
+    )
+
+    if not eval_only:
+        # run tuning tasks
+        print("Tuning...")
+        tune_tasks(tasks, **tuning_opt)
+
+    # compile kernels with history best records
+    log_file = os.path.join(log_dir, tuning_opt["log_filename"])
+    with autotvm.apply_history_best(log_file):
+        print("Compile...")
+        with tvm.transform.PassContext(opt_level=3):
+            lib = relay.build_module.build(mod, target=target)
+
+        # export library
+        tmp = tempdir()
+        if use_android:
+            from tvm.contrib import ndk
+            filename = "net.so"
+            lib.export_library(tmp.relpath(filename), ndk.create_shared)
+        else:
+            filename = "net.tar"
+            lib.export_library(tmp.relpath(filename))
+
+        # upload module to device
+        print("Upload...")
+        remote = autotvm.measure.request_remote(device_key, remote_host, remote_port, timeout=10000)
+        remote.upload(tmp.relpath(filename))
+        rlib = remote.load_module(filename)
+
+        # upload parameters to device
+        ctx = remote.context(str(target), 0)
+        module = runtime.GraphModule(rlib["default"](ctx))
+        dtype = "float32"
+        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
+        module.set_input("data", data_tvm)
+
+        # evaluate
+        print("Evaluate inference time cost...")
+        ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=10)
+        prof_res = np.array(ftimer().results) * 1000  # convert to millisecond
+        print(
+            "Mean inference time (std dev): %.2f ms (%.2f ms)"
+            % (np.mean(prof_res), np.std(prof_res))
+        )
+
+
+def main():
+    # val_path = '/home/ubuntu/tensorflow_datasets/downloads/manual/imagenet2012/val.rec'
+    val_path = '~/.mxnet/datasets/imagenet/rec/val.rec'
+    tuning_option = {
+        "tuner": "xgb",
+        "n_trial": 1500,
+        "early_stopping": 500,
+        "measure_option": autotvm.measure_option(
+            builder=autotvm.LocalBuilder(build_func="ndk" if use_android else "default"),
+            runner=autotvm.RPCRunner(
+                device_key,
+                host=remote_host,
+                port=remote_port,
+                number=5,
+                timeout=10,
+            ),
+        ),
+    }
+
+    if args.run_all:
+        models = ['resnet18_v1', 'mobilenet1.0', 'mobilenetv2_1.0']
+    else:
+        models = [args.model]
+    for model_name in models:
+        img_size = 299 if model_name == 'inceptionv3' else 224
+        val_data, batch_fn = get_val_data(img_size, val_path, batch_size)
+
+        if not args.skip_fp32:
+            fp32_mod, params, input_shape = get_model(model_name)
+            func = hago.prerequisite_optimize(fp32_mod['main'], params=params)
+            log_file = "%s.%s.f32.log" % (device_key, model_name)
+            tuning_option['log_filename'] = log_file
+            tune_and_evaluate(func, {}, input_shape, tuning_option)
+
+        # Quantize
+        calib_dataset = get_calibration_dataset(val_data, batch_fn, var_name='data')
+        fp32_mod, params, input_shape = get_model(model_name)
+        qconfig = hago.qconfig(use_channel_quantize=False,
+                               round_scale_to_pot=True,
+                               log_file='temp.log')
+        quantized_func = quantize_hago(fp32_mod, params, calib_dataset, qconfig)
+        quantized_func = relay.qnn.transform.CanonicalizeOps()(quantized_func)
+        print(quantized_func)
+
+        log_file = "%s.%s.i8.i32.log" % (device_key, model_name)
+        tuning_option['log_filename'] = log_file
+        tune_and_evaluate(quantized_func, {}, input_shape, tuning_option)
+
+if __name__ == '__main__':
+    main()