neuromorphic
siddha
SIDDHA
Smart Inertial Device Data from Human Activities
SIDDHA is a curated dataset derived from smartphone and smartwatch inertial measurement units (IMUs).It contains uniformly sampled accelerometer and gyroscope data at 20 Hz, for 51 subjects performing 18 activities across two devices:
Phone IMU data: accelerometer + gyroscope (x, y, z axes)
Watch IMU data: accelerometer + gyroscope (x, y, z axes)
All samples are timestamped, aligned, and stored in multiple formats:… See the full description on the dataset page: https://huggingface.co/datasets/neuromorphic-polito/siddha.neuromorphic-event-language-bridge
Neuromorphic Event-Language Bridge
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated event-language payload is now
published under data/raw/. It is available for… See the full description on the dataset page: https://huggingface.co/datasets/rmems/neuromorphic-event-language-bridge.neuromorphic-event-language-bridge-grok46
Neuromorphic Event-Language Bridge (Grok 4.6)
Rights & intended use: public research corpus, not training data.
Hosted frontier-model outputs are research-only inputs under project policy
(synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json. License:
Synthetic Factory Research-Only License v1.0 (license: other, see LICENSE) (non-commercial).
Release status:… See the full description on the dataset page: https://huggingface.co/datasets/rmems/neuromorphic-event-language-bridge-grok46.optical-neuromorphic-eikonal-benchmarks
Optical Neuromorphic Eikonal Solver - Benchmark Datasets
Overview
Benchmark datasets for evaluating the Optical Neuromorphic Eikonal Solver, a GPU-accelerated pathfinding algorithm achieving 30-300× speedup over CPU Dijkstra.
🎯 Key Results
134.9× average speedup vs CPU Dijkstra
0.64% mean error (sub-1% accuracy)
1.025× path length (near-optimal paths)
2-4ms per query on 512×512 grids
📊 Dataset Content
5 synthetic pathfinding test cases covering… See the full description on the dataset page: https://huggingface.co/datasets/Agnuxo/optical-neuromorphic-eikonal-benchmarks.neuromorphic_edge_computing_telemetryRoshamboThis dataset is a frame-based version of the ROSHAMBO17 dataset, originally collected for the work:
@inproceedings{lungu2017roshambo,
title={Live Demonstration: Convolutional Neural Network Driven by Dynamic Vision Sensor Playing RoShamBo},
author={Lungu, Iulia-Alexandra and Corradi, Federico and Delbruck, Tobi},
booktitle={2017 IEEE International Symposium on Circuits and Systems (ISCAS)},
year={2017}… See the full description on the dataset page: https://huggingface.co/datasets/neuromorphic-polito/Roshambo.