Datasets:

hardware-fab
/

osiris

Tasks:

Reinforcement Learning

Languages:

English

Tags:

eda

analog

License:

Dataset card Files Files and versions Community

Dataset Viewer

Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
spice: binary
__key__: string
__url__: string
sp: null
to
{'sp': Value(dtype='binary', id=None), '__key__': Value(dtype='string', id=None), '__url__': Value(dtype='string', id=None)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2270, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1888, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2215, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              spice: binary
              __key__: string
              __url__: string
              sp: null
              to
              {'sp': Value(dtype='binary', id=None), '__key__': Value(dtype='string', id=None), '__url__': Value(dtype='string', id=None)}
              because column names don't match

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Osiris: A Scalable Dataset Generation Pipeline for Machine Learning in Analog Circuit Design

Osiris is an end-to-end analog circuits design pipeline capable of producing, validating, and evaluating layouts for generic analog circuits.

The Osiris GitHub repository hosts the code that implements the randomized pipeline as well as the reinforcement learning-driven baseline methodology discussed in the paper proposed at the NeurIPS 2025 Datasets & Benchmarks Track.

The Osiris 🤗 HuggingFace repository hosts the randomly generated dataset discussed in the paper.

Curated by: hardware-fab
License: Open Data Commons License cc-by-4.0

How to Download

The dataset is stored in Osiris_Dataset.tar.

from huggingface_hub import hf_hub_download

file_path = hf_hub_download(
    repo_id="hardware-fab/osiris",
    filename="Osiris_Dataset.tar",
    repo_type="dataset",
    local_dir=<download_path>
)

Note

This repository is protected by copyright and licensed under the Apache-2.0 license file.

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