diff --git a/hpvm/test/epoch_dnn/assets/miniera/calib.txt b/hpvm/test/epoch_dnn/assets/miniera/calib.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4fff404f7e9b7beb3ef3dfc19bf1648c68dbe7be
--- /dev/null
+++ b/hpvm/test/epoch_dnn/assets/miniera/calib.txt
@@ -0,0 +1,21 @@
+input: 0.007874015748031496
+conv1: 0.041152522047244094
+add1: 0.041152522047244094
+relu1: 0.03673215196850394
+conv2: 0.18990387401574804
+add2: 0.18990387401574804
+relu2: 0.12851757480314963
+pool1: 0.12851757480314963
+conv3: 0.4379456692913386
+add3: 0.4379456692913386
+relu3: 0.13566622047244095
+conv4: 0.3754296456692913
+add4: 0.3754296456692913
+relu4: 0.15238466141732282
+pool2: 0.15238466141732282
+gemm1: 0.27306629921259845
+add5: 0.27306629921259845
+relu5: 0.20020370078740157
+gemm2: 0.6007037007874017
+add6: 0.6007037007874017
+softmax1: 0.007874015748031496
diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_1_b.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_1_b.bin
new file mode 100644
index 0000000000000000000000000000000000000000..39c3fbac7f94a6824736f8b21f184b71b3d45a7b
--- /dev/null
+++ b/hpvm/test/epoch_dnn/assets/miniera/conv2d_1_b.bin
@@ -0,0 +1,2 @@
+αÝ>aŸ¾Ì?N?„œQ¿JÙ%½t ‰¼ªl©=™&¼½œ ¿¿^L8?د > r¾:õö¾
νóù¼š¶?B Y?–;Uì>ç—=€ëh?rXö½ï
+‹=&ç½Ýˆ™½C#S>¥”½7ü¹>vÉ…>ɇ¿ !?
\ No newline at end of file
diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_1_w.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_1_w.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d01508286ed5fddf05790e261efa168847699efd
Binary files /dev/null and b/hpvm/test/epoch_dnn/assets/miniera/conv2d_1_w.bin differ
diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_2_b.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_2_b.bin
new file mode 100644
index 0000000000000000000000000000000000000000..39489675632774a46e0ea704d7d13807b2e4feb5
--- /dev/null
+++ b/hpvm/test/epoch_dnn/assets/miniera/conv2d_2_b.bin
@@ -0,0 +1,2 @@
+„§Ÿ½æ.î=•·?¾Š¥¿vƒ¿ºS¿Óë†»Þ >
+Qżøæ¼å—8¿ÂVä».I>Æp𼄃;dd=h䈾Ðé¾N½.¾ÓñÍ=/Ú¾ŒÖl¾×;¾ ð4¾6ƒ>cTʾR¶ ¼ê¿¾ô2Í=c_¨¾¾ÚZ¾
\ No newline at end of file
diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_2_w.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_2_w.bin
new file mode 100644
index 0000000000000000000000000000000000000000..381b72379b85614a79910c9560c6115310da538a
Binary files /dev/null and b/hpvm/test/epoch_dnn/assets/miniera/conv2d_2_w.bin differ
diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_3_b.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_3_b.bin
new file mode 100644
index 0000000000000000000000000000000000000000..43fe41a6edcae03a1a531123940e528a71807300
--- /dev/null
+++ b/hpvm/test/epoch_dnn/assets/miniera/conv2d_3_b.bin
@@ -0,0 +1 @@
+˜Ò½Ê+¥½ò?Š$ù¾méÊ>(>¼½hŠ >qÂB½y²*½‚ì>IÒ¸>»Kˆ?@ ¨¼t\¢?æ H¾
•=ùÔý>…œ;½_å—>Ÿfœ=;┿®Œû>›jÞ¾DâÓ»×Á‰¾šU>·`†? Éc>ÈŽ?Õª?Ÿ·/< #&?—?ôðš¾Dy<Hbf¾lò:?ÑwS¾M
)>}¾«t ¾Ÿt'?ö¥ú¿´$¾<¥!æ½ œ?Ë¡½c’‹<Za¾>E5“>0Jê>p9J¾†žÁ>ÞWð>nèr>e–'?¹+R¿Gž>kæò;·bÆ¿²àR>˜{ÿ>ãš½
\ No newline at end of file
diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_3_w.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_3_w.bin
new file mode 100644
index 0000000000000000000000000000000000000000..a82a9e397918217bf37873c59dd92bee713fa9df
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diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_4_b.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_4_b.bin
new file mode 100644
index 0000000000000000000000000000000000000000..bd29ee60df1f0f0b3a199f1f65adb810a2649a3c
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diff --git a/hpvm/test/epoch_dnn/assets/miniera/conv2d_4_w.bin b/hpvm/test/epoch_dnn/assets/miniera/conv2d_4_w.bin
new file mode 100644
index 0000000000000000000000000000000000000000..7f6211e76617ee18dac06c9b5449c18183a149de
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diff --git a/hpvm/test/epoch_dnn/assets/miniera/dense_1_b.bin b/hpvm/test/epoch_dnn/assets/miniera/dense_1_b.bin
new file mode 100644
index 0000000000000000000000000000000000000000..fae8736fa3da691229c66e73962cb4f0131c2961
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diff --git a/hpvm/test/epoch_dnn/assets/miniera/dense_1_w.bin b/hpvm/test/epoch_dnn/assets/miniera/dense_1_w.bin
new file mode 100644
index 0000000000000000000000000000000000000000..034b84905ae34893f5b57be77033fd91b388a80b
Binary files /dev/null and b/hpvm/test/epoch_dnn/assets/miniera/dense_1_w.bin differ
diff --git a/hpvm/test/epoch_dnn/assets/miniera/dense_2_b.bin b/hpvm/test/epoch_dnn/assets/miniera/dense_2_b.bin
new file mode 100644
index 0000000000000000000000000000000000000000..2bb9d3e89f7d35a4e66063ec93baab23ef90b86e
--- /dev/null
+++ b/hpvm/test/epoch_dnn/assets/miniera/dense_2_b.bin
@@ -0,0 +1 @@
+k"õ;(¿¾òë>™¿JÄ@?
\ No newline at end of file
diff --git a/hpvm/test/epoch_dnn/assets/miniera/dense_2_w.bin b/hpvm/test/epoch_dnn/assets/miniera/dense_2_w.bin
new file mode 100644
index 0000000000000000000000000000000000000000..f3bce42e01c37928d0cd54493835bcda70529bf2
Binary files /dev/null and b/hpvm/test/epoch_dnn/assets/miniera/dense_2_w.bin differ
diff --git a/hpvm/test/epoch_dnn/assets/miniera/input.bin b/hpvm/test/epoch_dnn/assets/miniera/input.bin
new file mode 100644
index 0000000000000000000000000000000000000000..0abae55bf84ff5dc8e2d1074c97853331fc5d879
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diff --git a/hpvm/test/epoch_dnn/assets/miniera/labels.bin b/hpvm/test/epoch_dnn/assets/miniera/labels.bin
new file mode 100644
index 0000000000000000000000000000000000000000..effaef8583b30228039ff7f61d9c6be51c020b49
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diff --git a/hpvm/test/epoch_dnn/assets/miniera/miniera.pth b/hpvm/test/epoch_dnn/assets/miniera/miniera.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f264a4ea67f88c361923e7c7f0208703001a622d
Binary files /dev/null and b/hpvm/test/epoch_dnn/assets/miniera/miniera.pth differ
diff --git a/hpvm/test/epoch_dnn/main.py b/hpvm/test/epoch_dnn/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f9e5795d323359a5a798abb0aa985bc8c91e224
--- /dev/null
+++ b/hpvm/test/epoch_dnn/main.py
@@ -0,0 +1,18 @@
+import site
+from pathlib import Path
+
+import torch
+from torch2hpvm import BinDataset, ModelExporter
+
+self_folder = Path(__file__).parent.absolute()
+site.addsitedir(self_folder)
+
+from torch_dnn import MiniERA
+
+asset_dir = self_folder / "assets/miniera"
+output_dir = Path("/tmp/miniera")
+bin_dataset = BinDataset(asset_dir / "input.bin", asset_dir / "labels.bin", (5000, 3, 32, 32))
+model = MiniERA()
+model.load_state_dict(torch.load(asset_dir / "miniera.pth"))
+exporter = ModelExporter(model, bin_dataset, output_dir, asset_dir / "calib.txt")
+exporter.generate().compile(output_dir / "miniera", output_dir)
diff --git a/hpvm/test/epoch_dnn/make_ckpt.py b/hpvm/test/epoch_dnn/make_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..eda4c2c02a63cd181e1a1a8552865ba7b72d26d2
--- /dev/null
+++ b/hpvm/test/epoch_dnn/make_ckpt.py
@@ -0,0 +1,33 @@
+"""Make PyTorch checkpoint of MiniERA model from legacy HPVM weights."""
+import site
+from pathlib import Path
+
+import torch
+
+self_folder = Path(__file__).parent.absolute()
+site.addsitedir(self_folder)
+
+from torch_dnn import CIFAR, MiniERA
+
+
+@torch.no_grad()
+def main():
+ prefix = self_folder / "assets/miniera"
+ model = MiniERA().load_legacy_hpvm_weights(prefix)
+ # Test mini ERA
+ dataset = CIFAR.from_file(prefix / "input.bin", prefix / "labels.bin")
+ dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False)
+ correct = 0
+ total = 0
+ for data in dataloader:
+ images, labels = data[0], data[1]
+ outputs = model(images)
+ _, predicted = torch.max(outputs.data, 1)
+ total += labels.size(0)
+ correct += (predicted == labels).sum().item()
+ print(f"Accuracy of the network on the test images: {100 * correct / total} %")
+ torch.save(model.state_dict(), prefix / "miniera.pth")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/hpvm/test/epoch_dnn/torch_dnn/__init__.py b/hpvm/test/epoch_dnn/torch_dnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..62f29d0d7e715cceb1679f3dde21bc11e22dc131
--- /dev/null
+++ b/hpvm/test/epoch_dnn/torch_dnn/__init__.py
@@ -0,0 +1,2 @@
+from .datasets import CIFAR
+from .miniera import MiniERA
diff --git a/hpvm/test/epoch_dnn/torch_dnn/datasets.py b/hpvm/test/epoch_dnn/torch_dnn/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac519bfe26ba48429cf40d6f16fdc3587e606b37
--- /dev/null
+++ b/hpvm/test/epoch_dnn/torch_dnn/datasets.py
@@ -0,0 +1,99 @@
+import logging
+from pathlib import Path
+from typing import Iterator, Tuple, Union
+
+import numpy as np
+import torch
+from torch.utils.data.dataset import Dataset
+
+RetT = Tuple[torch.Tensor, torch.Tensor]
+msg_logger = logging.getLogger(__name__)
+
+PathLike = Union[Path, str]
+
+
+class SingleFileDataset(Dataset):
+ def __init__(self, inputs: torch.Tensor, outputs: torch.Tensor):
+ self.inputs, self.outputs = inputs, outputs
+
+ @classmethod
+ def from_file(cls, *args, **kwargs):
+ pass
+
+ @property
+ def sample_input(self):
+ inputs, outputs = next(iter(self))
+ return inputs
+
+ def __len__(self) -> int:
+ return len(self.inputs)
+
+ def __getitem__(self, idx) -> RetT:
+ return self.inputs[idx], self.outputs[idx]
+
+ def __iter__(self) -> Iterator[RetT]:
+ for i in range(len(self)):
+ yield self[i]
+
+
+class DNNDataset(SingleFileDataset):
+ image_shape = None
+ label_ty = np.int32
+
+ @classmethod
+ def from_file(
+ cls,
+ input_file: PathLike,
+ labels_file: PathLike,
+ count: int = -1,
+ offset: int = 0,
+ ):
+ # NOTE: assuming (N, *) ordering of inputs (such as NCHW, NHWC)
+ channel_size = np.prod(np.array(cls.image_shape))
+ inputs_count_byte = -1 if count == -1 else count * channel_size
+ inputs = read_tensor_from_file(
+ input_file,
+ -1,
+ *cls.image_shape,
+ count=inputs_count_byte,
+ offset=offset * channel_size,
+ )
+ labels = read_tensor_from_file(
+ labels_file,
+ -1,
+ read_ty=cls.label_ty,
+ cast_ty=np.long,
+ count=count,
+ offset=offset,
+ )
+ if inputs.shape[0] != labels.shape[0]:
+ raise ValueError("Input and output have different number of data points")
+ msg_logger.info(f"%d entries loaded from dataset.", inputs.shape[0])
+ return cls(inputs, labels)
+
+
+class MNIST(DNNDataset):
+ image_shape = 1, 28, 28
+
+
+class CIFAR(DNNDataset):
+ image_shape = 3, 32, 32
+
+
+class ImageNet(DNNDataset):
+ image_shape = 3, 224, 224
+
+
+def read_tensor_from_file(
+ filename: Union[str, Path],
+ *shape: int,
+ read_ty=np.float32,
+ cast_ty=np.float32,
+ count: int = -1,
+ offset: int = 0,
+) -> torch.Tensor:
+ offset = offset * read_ty().itemsize
+ mmap = np.memmap(filename, dtype=read_ty, mode="r", offset=offset)
+ n_entries = min(mmap.shape[0], count) if count != -1 else mmap.shape[0]
+ np_array = mmap[:n_entries].reshape(shape).astype(cast_ty)
+ return torch.from_numpy(np_array).clone()
diff --git a/hpvm/test/epoch_dnn/torch_dnn/miniera.py b/hpvm/test/epoch_dnn/torch_dnn/miniera.py
new file mode 100644
index 0000000000000000000000000000000000000000..84c88ac8f882b4b6d1ba98cdb3a88f92cf418075
--- /dev/null
+++ b/hpvm/test/epoch_dnn/torch_dnn/miniera.py
@@ -0,0 +1,55 @@
+from pathlib import Path
+from typing import Union
+
+import numpy as np
+import torch
+from torch.nn import Conv2d, Linear, MaxPool2d, Module, ReLU, Sequential, Softmax
+
+
+class MiniERA(Module):
+ def __init__(self):
+ super().__init__()
+ self.convs = Sequential(
+ Conv2d(3, 32, 3),
+ ReLU(),
+ Conv2d(32, 32, 3),
+ ReLU(),
+ MaxPool2d(2, 2),
+ Conv2d(32, 64, 3),
+ ReLU(),
+ Conv2d(64, 64, 3),
+ ReLU(),
+ MaxPool2d(2, 2),
+ )
+ self.fcs = Sequential(Linear(1600, 256), ReLU(), Linear(256, 5))
+ self.softmax = Softmax(1)
+
+ def forward(self, input):
+ outputs = self.convs(input)
+ outputs = self.fcs(outputs.flatten(1, -1))
+ return self.softmax(outputs)
+
+ def load_legacy_hpvm_weights(self, prefix: Union[Path, str]):
+ prefix = Path(prefix)
+ # Load in model convolution weights
+ count = 0
+ for conv in self.convs:
+ if not isinstance(conv, Conv2d):
+ continue
+ weight_np = np.fromfile(prefix / "conv2d_{count+1}_w.bin", dtype=np.float32)
+ bias_np = np.fromfile(prefix / "conv2d_{count+1}_b.bin", dtype=np.float32)
+ conv.weight.data = torch.tensor(weight_np).reshape(conv.weight.shape)
+ conv.bias.data = torch.tensor(bias_np).reshape(conv.bias.shape)
+ count += 1
+ # Load in model fc weights
+ count = 0
+ for linear in self.fcs:
+ if not isinstance(linear, Linear):
+ continue
+ weight_np = np.fromfile(prefix / "dense_{count+1}_w.bin", dtype=np.float32)
+ bias_np = np.fromfile(prefix / "dense_{count+1}_b.bin", dtype=np.float32)
+ cout, cin = linear.weight.shape
+ linear.weight.data = torch.tensor(weight_np).reshape(cin, cout).T
+ linear.bias.data = torch.tensor(bias_np).reshape(linear.bias.shape)
+ count += 1
+ return self