open-neuromorphic/snnmetrics
Metrics for spiking neural networks based on torchmetrics
This package helps neuromorphic engineers and researchers quantify the computational efficiency of spiking neural networks (SNNs). It takes the activation data from individual SNN layers and calculates metrics like the number of synaptic operations (SynOps). This allows you to evaluate and compare different SNN architectures based on their operational cost.
No commits in the last 6 months.
Use this if you are developing or analyzing spiking neural networks and need to measure their computational workload in terms of synaptic operations.
Not ideal if you are working with traditional artificial neural networks (ANNs) or require metrics unrelated to the unique characteristics of spiking neurons.
Stars
13
Forks
1
Language
Python
License
—
Category
Last pushed
Mar 27, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/open-neuromorphic/snnmetrics"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fangwei123456/spikingjelly
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
neuromorphs/NIR
Neuromorphic Intermediate Representation reference implementation
BindsNET/bindsnet
Simulation of spiking neural networks (SNNs) using PyTorch.
norse/norse
Deep learning with spiking neural networks (SNNs) in PyTorch.
jeshraghian/snntorch
Deep and online learning with spiking neural networks in Python