Vision

SKaiNET aims to democratize "Edge AI / On-device AI" by bridging the gap between high-level application development and low-level hardware optimization. We believe AI should be portable, type-safe, and developer-friendly, enabling seamless intelligence in everything from mobile apps to IoT devices without sacrificing performance.

For architecture details see ARCHITECTURE.md.

---

Quickstart

Add the core dependencies (Gradle Kotlin DSL):

dependencies {
    implementation("sk.ainet.core:SKaiNET-lang-core:0.17.0")
    implementation("sk.ainet.core:SKaiNET-backend-cpu:0.17.0")
}

Java / Maven users — see Java Getting Started for BOM setup and JVM flags.

Hello Neural Net

val model = nn {
    input(28 * 28)
    dense(out = 128)
    relu()
    dense(out = 10)
}

Core Tensor Ops

val a = tensor(shape(2, 2)) { float(1f, 2f, 3f, 4f) }
val b = tensor(shape(2, 2)) { float(5f, 6f, 7f, 8f) }

val c = a matMul b
val d = c.relu()

GGUF Model Loading

val source = SystemFileSystem.source(Path("model.gguf")).buffered()
val reader = GGUFReader(source)

val tensor = reader.tensors.first { it.name == "token_embd.weight" }
val weights = reader.materialize(tensor)

More examples: SKaiNET-examples | SKaiNET-notebook

---

Ecosystem

SKaiNET is a modular ecosystem. While this repository contains the core engine, specialized high-level libraries are maintained in standalone repositories:

Project	Description
SKaiNET-LLM	Llama, Gemma, and BERT inference runtimes
SKaiNET-transformers	Pre-built transformer architectures and layers
SKaiNET-examples	Sample projects and integration demos

---

Explore

Goal	Start here
Examples and sample projects	SKaiNET-examples
Interactive notebooks	SKaiNET-notebook
LLM inference (Llama, Gemma)	SKaiNET-LLM
Java 21+ integration	docs/java-getting-started.md
Data loading and transforms	docs/io-readers-guide.md
Graph DSL (ResNet, YOLO)	docs/graph-dsl.md
Edge AI / Arduino export	docs/arduino-c-codegen.md
MLIR / StableHLO compiler	docs/hlo-getting-started.md
Architecture overview	ARCHITECTURE.md
Contributing	CONTRIBUTING.md

---

Features

Kotlin Multiplatform

• Targets: JVM, macOS (Native), JS, WASM (Browser + WasmWasi)
• Single codebase shared across all platforms via Kotlin Multiplatform

Optimized Execution

• ComputeGraphExecutor: Optimized engine with fusion passes and trace-to-DAG bridging.
• SDPA & Gather: High-performance Scaled Dot-Product Attention and indexing operations.

Agentic AI Infrastructure

• ComputeGraph: Unified framework for defining agentic workflows and tool-calling loops.
• Java facade: JavaAgentLoop (in skainet-lang-java)

Neural Network DSL

• Sequential: nn { input(); dense(); relu(); dense() }
• DAG / Graph: arbitrary wiring with dag { } for ResNet, YOLO-style architectures
• Layers: Dense, Conv1d/2d/3d, MaxPool, AvgPool, BatchNorm, Dropout, LeakyReLU, ELU
• KAN (Kolmogorov–Arnold Networks) layer (experimental)
• Autograd engine with reverse-mode gradients, SGD and Adam/AdamW optimizers

Data and I/O

• Built-in loaders: MNIST, Fashion-MNIST, CIFAR-10
• Formats: GGUF, ONNX, SafeTensors, JSON, Image (JPEG, PNG)
• Type-safe transform DSL: resize, crop, normalize, toTensor

Java 21+ Support

• SKaiNET entry point, TensorJavaOps, builder-pattern model definition
• Maven BOM (sk.ainet:skainet-bom) for one-line version management
• Docs: Getting Started | Model Training

Edge AI: Arduino / C99 Export

• Export trained models to standalone, optimized C99 with static memory allocation
• Ready-to-use Arduino library output
• See arduino-c-codegen.md

Compiler: MLIR / StableHLO

• Lower Kotlin DSL to MLIR StableHLO dialect
• Optimization passes: constant folding, operation fusion, dead code elimination
• Valid IREE-compilable output with streaming API and public HloGenerator
• See hlo-getting-started.md

---

What's New in 0.17.0

• Core Engine Focus — Refactored the repository to focus on the core ComputeGraph framework, compiler, and backends. Extracted high-level LLM and transformer implementations to standalone repositories.
• LLM-as-DSL — New high-level DSL for defining and running LLM architectures within the core framework.
• Optimized ComputeGraphExecutor — New executor with support for fusion passes and trace-to-DAG bridging for faster inference.
• SDPA & Gather — Implemented Scaled Dot-Product Attention and gather/indexSelect ops for improved performance.

See CHANGELOG.md for the full release history.

---

Roadmap

• Q1 2026: Comprehensive documentation ✅
• Q2 2026: Reference-based validation of computation correctness
• Q3 2026: Agentic AI enhancements ✅ (tool calling shipped in 0.13.0; ongoing)
• Q4 2026: Federated learning support for multi-device training

---

Contributing & Community

We love contributions! Whether it's a new operator, documentation, or a bug fix:

1. Read our Contribution Guide.
2. Check the Good First Issues.
3. Open a discussion or issue on GitHub.

Browse the full codebase documentation on DeepWiki.

Contributors (0.14.0)

• Dhia Chemingui (@dhiaspaner) — Android KMP plugin migration (#385, #386)

---

License

MIT — see LICENCE.