datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Chameleon
Chameleon
Chameleon is a dataset designed for side-channel analysis of obfuscated power traces.
It contains real-world power traces collected from a 32-bit RISC-V System-on-Chip implementing four hiding countermeasures:
Dynamic Frequency Scaling (DFS), Random Delay (RD), Morphing (MRP), and Chaffing (CHF).
The dataset also includes side-channel power traces without any active countermeasure (BASE).
Each side-channel trace includes multiple cryptographic operations
interleaved… See the full description on the dataset page: https://huggingface.co/datasets/hardware-fab/Chameleon.unitree-g1-hardware-modifications
Unitree G1 Hardware Modifications
Custom 3D-printable parts and reference photos for modifying the Unitree G1: OpenArm
grippers on the wrists, an extra head camera, and a backpack enclosure for the CAN-FD interface
and cabling.
Everything here is printable geometry plus photos of the assembled result — there is no
firmware or control code in this repo.
Modifications
1. Gripper on the G1 wrist
The gripper itself is the stock gripper from the OpenArm… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/unitree-g1-hardware-modifications.TroLL-Logic-Locking-based-Hardware-TrojansDFS_DESYNCH
DFS_DESYNCH
The DFS_DESYNCH dataset contains power traces of a software AES implementation running on a 32-bit RISC-V System-on-Chip (SoC).
The SoC incorporates a Dynamic Frequency Scaling (DFS) unit that randomly adjusts the operating frequency between 35MHz and 60MHz.
Curated by: hardware-fab
License: Open Data Commons License cc-by-4.0
Repository: DLaTA methodology GitHub
Paper: A Deep Learning-assisted Template Attack Against Dynamic Frequency Scaling Countermeasures
The… See the full description on the dataset page: https://huggingface.co/datasets/hardware-fab/DFS_DESYNCH.openarms-hardware-modifications
OpenArm Hardware Modifications for Cloth Folding
Custom 3D-printable parts used in the Unfolding Robotics project, where we trained a bimanual robot to fold t-shirts with a 90% success rate.
These files modify the standard OpenArm.
Files
File
Description
J4_5cm_extended.step
Extended upper arm (bicep) segment, adds +5 cm of reach to compensate for the lack of a hip/torso in our setup. STEP format for easy modification.
J3-J4_Cover front extended.stl
Front… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/openarms-hardware-modifications.hardware-cvdp-complete
CVDP - Comprehensive Verilog Design Problems (Complete Dataset)
🎯 782 out of 783 problems from the official CVDP benchmark by NVIDIA Research
🔥 Dataset Overview
This is the most complete version of the Comprehensive Verilog Design Problems (CVDP) benchmark available, containing 782 problems across 13 task categories. CVDP is designed to evaluate Large Language Models and agents on RTL design and verification tasks.
📊 Dataset Statistics
Total Problems: 772… See the full description on the dataset page: https://huggingface.co/datasets/AbiralArch/hardware-cvdp-complete.hardware_code_and_sec_medianhardware_code_and_sec_smallosiris
OSIRIS: Bridging Analog Layout Circuit Design and Machine Learning with Scalable Dataset Generation
OSIRIS is an end-to-end analog circuit design pipeline capable of producing, validating, and evaluating large volumes of layouts for generic analog circuits.
The OSIRIS 🤗 HuggingFace repository hosts the randomly generated dataset discussed in the paper.
This codebse provides the OSIRIS Python script (osiris.py) and the Python friendly version of the SkyWater 130nm PDK (SKY130_PDK)… See the full description on the dataset page: https://huggingface.co/datasets/hardware-fab/osiris.diy-project-code-based-on-hardware-imageHoloMotion_hardwarehardware-verilogeval-v2
hardware-verilogeval-v2
VerilogEval v2 - 471 Verilog evaluation problems
Dataset Overview
This dataset is part of a comprehensive collection of hardware design datasets for training and evaluating LLMs on Verilog/SystemVerilog code generation and hardware design tasks.
Files
verilog_eval_problems.json: 471 VerilogEval v2 problems
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset('AbiralArch/hardware-verilogeval-v2')… See the full description on the dataset page: https://huggingface.co/datasets/AbiralArch/hardware-verilogeval-v2.hardware-cvdp-problems
Hardware Design AI Training Dataset
This dataset contains processed hardware design problems and Verilog code for training AI models.
Contents
CVDP Problems: 160 evaluation problems organized by domain and complexity
Training Data: Instruction-code pairs for hardware design
Metadata: Rich annotations for each problem
Usage
from datasets import load_dataset
dataset = load_dataset("AbiralArch/hardware-cvdp-problems")
Categories
Module Generation… See the full description on the dataset page: https://huggingface.co/datasets/AbiralArch/hardware-cvdp-problems.quantum-hardware-device-physics
Neura Parse — Quantum Hardware Device Physics: Qubit Design, Coherence, Control & Scaling
A physics- and engineering-deep vertical on how qubits are built, controlled, and scaled across superconducting, trapped-ion, neutral-atom, and spin modalities (plus emerging erasure/biased-noise qubits). Device-physics derivations, coherence-limit analyses, control-stack engineering, and 2025-2026 scaling/interconnect work, with QuTiP/scqubits simulation context — expanding the general… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-hardware-device-physics.modelfit-hardware-dataset
ModelFit: Local LLM Hardware Compatibility Dataset
An open dataset of which local AI models (Ollama) fit which hardware, by
parameter size, quantization, minimum RAM, and estimated memory load, across
Apple Silicon Macs, iPhones, and NVIDIA GPUs.
Maintained by ModelFit. Browse it as an interactive
table at modelfit.io/data; the canonical
machine-readable source is
modelfit.io/api/dataset.
141 models across 24 families (106 with a registry-verified local build, 35 cloud-only… See the full description on the dataset page: https://huggingface.co/datasets/modelfit/modelfit-hardware-dataset.Hardware-Rooted-AI-HALLUCINATION-FINALITY-STOPPING-UNSAFE-AGENT-ACTS-BEFORE-EFFECTUATION
AI Hallucination Finality Layer
Stopping hallucinated, stale, and unsafe agent acts before they become external consequences.
Computation does not imply consequence.
## Abstract
Artificial intelligence is rapidly moving from generating information to causing consequences.
AI agents can now initiate payments, modify databases, deploy software, send communications, call external tools, change network configurations, control infrastructure, update models, and… See the full description on the dataset page: https://huggingface.co/datasets/sangamdas/Hardware-Rooted-AI-HALLUCINATION-FINALITY-STOPPING-UNSAFE-AGENT-ACTS-BEFORE-EFFECTUATION.Manufacturing-Hardware-and-MaterialsCommercial License Available: This is a free evaluation subset of a 71,777-image dataset. To acquire the full commercial dataset (100% clean-room guaranteed, zero PII, zero copyright risk), visit outpostvertical.com/manufacturing to purchase via Enterprise PO, or email data@outpostvertical.com for a SWIFT wire invoice.
EVALUATION SUBSET: The 2026 Manufacturing Hardware & Materials Vision Pack
1. Evaluation Dataset Overview
Eval Subset Image Count: 410 Images… See the full description on the dataset page: https://huggingface.co/datasets/outpostvertical/Manufacturing-Hardware-and-Materials.r1-h4-trigger-hardware-15fpsthe-double-hardware-imperative-why-ilya-sutskevers-ssi-is-making-the-he-goat-the-gardener
The Double-Hardware Imperative: Why Ilya Sutskever's SSI is Making the He-Goat the Gardener
COPYRIGHT NOTICE (ALL RIGHTS RESERVED)
© 2026 Selçuk Cara / C.A.R.A. Institute.
All rights reserved. Unauthorized copying, redistribution, or AI training on this data is strictly prohibited.
Zenodo Reference
Official Research Link: [Insert your Zenodo link here]
Technical & Historical Framework
https://zenodo.org/records/22335918
The Double-Hardware… See the full description on the dataset page: https://huggingface.co/datasets/c-a-r-a-institut/the-double-hardware-imperative-why-ilya-sutskevers-ssi-is-making-the-he-goat-the-gardener.firewall-hardware-end-of-life-dates-by-brand
Firewall and network security appliance hardware end-of-life dates by brand
Canonical, always-current version: https://referencesource.org/firewall-hardware-end-of-life-dates-by-brand/
Machine-readable: https://referencesource.org/firewall-hardware-end-of-life-dates-by-brand/data.json — this mirror is a point-in-time copy.
Last verified: 2026-08-26
Stale after: 2027-02-22 (past this date, prefer the canonical copy —
it re-verifies on a cadence this snapshot does not)
Records:… See the full description on the dataset page: https://huggingface.co/datasets/referencesource/firewall-hardware-end-of-life-dates-by-brand.ibm-150q-ising-hardware-results
IBM 150-Qubit Ising Hardware Results
Auditable results from a hardware-native, 150-active-qubit Ising/QAOA study on IBM ibm_fez, accompanied by an exact classical baseline and explicit scientific claim boundaries.
Headline evidence
Item
Verified value
Active physical qubits
150
Processor qubits
156
Successful hardware jobs
15
QPU usage
599 seconds
PUBs
547
Shots
2,087,936
QAOA depths
p=1 through p=8
Exact classical optimum
865 / 873, MIP… See the full description on the dataset page: https://huggingface.co/datasets/sankalpsthakur/ibm-150q-ising-hardware-results.ai-inference-hardware-economics-2026
🚀 2026 AI Inference & Hardware Economics Telemetry Index
This repository hosts the official open-access empirical telemetry dataset for 2026 AI Inference, Silicon Architecture, and Hardware Economics, curated by EyesTech Systems & FinOps Intelligence.
Original Research Investigation:For the complete whitepaper, interactive latency calculators, and per-token TCO models, see the flagship publication at:👉… See the full description on the dataset page: https://huggingface.co/datasets/devidasmishra/ai-inference-hardware-economics-2026.africa-synth-telecom-hardware-sensor-data-nigeria
Africa Synth Telecom Hardware Sensor Data Nigeria | Africa (Electric Sheep Africa metadata inventory)
Size category: 100K<n<1M - Formats: parquet - Sector: energy - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-telecom-hardware-sensor-data-nigeria.digit3a_hardware_door_open_rgb_datasetThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "digit_third_arm",
"total_episodes": 33,
"total_frames": 13730,
"total_tasks": 1,
"total_videos": 33,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:33"},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kelvinzhaozg/digit3a_hardware_door_open_rgb_dataset.africa-synth-telecom-hardware-sensor-data-nigeria
Africa Synthetic Telecom Hardware Sensor Data Nigeria (TsFile)
This dataset is an Apache TsFile conversion of electricsheepafrica/africa-synth-telecom-hardware-sensor-data-nigeria, a synthetic Nigerian telecom tower hardware sensor dataset with temperature, power, voltage, humidity, vibration, health-status, and alert readings.
Source Dataset
Original dataset: electricsheepafrica/africa-synth-telecom-hardware-sensor-data-nigeria
Source files:… See the full description on the dataset page: https://huggingface.co/datasets/THULab/africa-synth-telecom-hardware-sensor-data-nigeria.hardwarerecs.stackexchange.comMlops-Hardware-Carbon-Benchmarks
MLOps Hardware Benchmarks & Carbon Emissions
Dataset Description
This dataset contains 3,000 empirical, synthetic profiling records tracking large language model execution runs across diverse modern datacenter and consumer accelerators (including NVIDIA H100, A100, RTX 4090, and A10G). It captures token volumes, execution speeds, physical power utilization metrics, and overall computed carbon footprint weights.
Purpose and Impact
As deep learning… See the full description on the dataset page: https://huggingface.co/datasets/sohaibdevv/Mlops-Hardware-Carbon-Benchmarks.modelfit-hardware-dataset
ModelFit Local LLM Hardware Compatibility Dataset
Which local AI models fit which hardware. Maps 107 LLMs (75 of them local-capable via Ollama, llama.cpp or LM Studio) to RAM/VRAM requirements at Q4_K_M quantization, so you can look up "will this model run on my machine" without guessing.
Source of truth: modelfit.io/data. This dataset is a mirror of the live JSON export at modelfit.io/api/dataset, refreshed from the same GitHub repo that generates it:… See the full description on the dataset page: https://huggingface.co/datasets/weckoai/modelfit-hardware-dataset.piper_scoop_v1_20260721_154509This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"joint_1.pos",
"joint_2.pos",
"joint_3.pos",
"joint_4.pos",
"joint_5.pos",
"joint_6.pos",
"gripper.pos"
],
"shape": [… See the full description on the dataset page: https://huggingface.co/datasets/hardware-pathon-ai/piper_scoop_v1_20260721_154509.openarm_dataset_test_20260920_214839This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 15,
"features": {
"action": {
"dtype": "float32",
"names": [
"joint_1.pos",
"joint_2.pos",
"joint_3.pos",
"joint_4.pos",
"joint_5.pos",
"joint_6.pos",
"joint_7.pos",
"gripper.pos"
]… See the full description on the dataset page: https://huggingface.co/datasets/hardware-pathon-ai/openarm_dataset_test_20260920_214839.