Lava
Lava Architecture
Key attributes
Why do we need Lava?
Lava’s foundational concepts
1. Processes:
2. Behavioral implementations via ProcessModels:
3. Composability and connectivity:
4. Cross-platform execution:
Lava software stack
Getting Started With Lava
Application examples:
Fundamental concepts:
Algorithms and Application Libraries
Deep Learning
Introduction
Lava-DL Workflow
Getting Started
SLAYER 2.0
Example Code
Bootstrap
Example Code
Network Exchange (NetX) Library
Example Code
Detailed Description
Lava-DL SLAYER
Lava-DL Bootstrap
Lava-DL NetX
Dynamic Neural Fields
Introduction
What is lava-dnf?
Key features
Example
Neuromorphic Constraint Optimization
Tutorials
QP Solver
Example
QP Solver
Coming up next: CSPSolver
Requirements
Setup
Developer Guide
Lava’s Origins
Contact Information
Table of Contents
Development Roadmap
Initial Release
How to contribute to Lava
Open an Issue
Pull Request Checklist
Open a Pull Request
Coding Conventions
Code Requirements
Guidelines
Docstring Format
Contributors
Contributor
Committer
List of lava-nc/lava Project Committers
List of lava-nc/lava-dnf Project Committers
List of lava-nc/lava-optimization Project Committers
List of lava-nc/lava-dl Project Committers
Committer Promotion
Repository Structure
lava-nc/lava
lava-nc/lava-dnf
lava-nc/lava-dl
lava-nc/lava-optimization
lava-nc/lava-docs
Code of Conduct
Licenses
Lava API Documentation
Lava
Lava - Deep Learning
SLAYER
Neuron
Synapse
Spike
Axon
Dendrite
Blocks
Loss
Classifier
Input/Output
Auto
Utilities
Indices and tables
Bootstrap (ANN-SNN training)
Blocks
ANN Statistics Sampler
Routine
Indices and tables
Lava-DL NetX
Blocks
HDF5
Utils
Indices and tables
Lava - Dynamic Neural Fields
Lava - Optimization
Lava
»
Lava API Documentation
»
Lava - Deep Learning
»
Lava-DL NetX
»
Blocks
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Blocks
Blocks Module
Process
Models
Module contents