# CNN2SNN toolkit
The Brainchip CNN2SNN toolkit provides a means to convert Convolutional Neural
Networks (CNN) that were trained using Deep Learning methods to a low-latency
and low-power Spiking Neural Network (SNN) for use with the Akida Runtime.
Raw data
{
"_id": null,
"home_page": "https://doc.brainchipinc.com",
"name": "cnn2snn",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "",
"author": "Alvaro Moran",
"author_email": "amoran@brainchip.com",
"download_url": "https://files.pythonhosted.org/packages/87/8a/29b9782e809fb088cbc1082a2c1c14c91014b2653215957cf08cd54d7b11/cnn2snn-2.6.0.tar.gz",
"platform": null,
"description": "# CNN2SNN toolkit\n\nThe Brainchip CNN2SNN toolkit provides a means to convert Convolutional Neural\nNetworks (CNN) that were trained using Deep Learning methods to a low-latency \nand low-power Spiking Neural Network (SNN) for use with the Akida Runtime.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Keras to Akida CNN Converter",
"version": "2.6.0",
"project_urls": {
"Homepage": "https://doc.brainchipinc.com"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "878a29b9782e809fb088cbc1082a2c1c14c91014b2653215957cf08cd54d7b11",
"md5": "9b229f13eff403c8653ae0048b026785",
"sha256": "4855e5d3e33ffa234d2b430b0abe9ea49ebff8aa3206565ff9410002f60c90ad"
},
"downloads": -1,
"filename": "cnn2snn-2.6.0.tar.gz",
"has_sig": false,
"md5_digest": "9b229f13eff403c8653ae0048b026785",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 83485,
"upload_time": "2023-11-28T16:18:52",
"upload_time_iso_8601": "2023-11-28T16:18:52.697488Z",
"url": "https://files.pythonhosted.org/packages/87/8a/29b9782e809fb088cbc1082a2c1c14c91014b2653215957cf08cd54d7b11/cnn2snn-2.6.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-11-28 16:18:52",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "cnn2snn"
}
JSON