Selected work
AetherMind
AetherMind
Overview
AetherMind is an experimental neural network and machine learning framework written entirely in Rust.
The long-term vision of AetherMind is to become a modular, extensible and efficient framework for building, training and deploying neural networks while taking full advantage of Rust's safety, concurrency and performance.
The project focuses on implementing every major subsystem from scratch, allowing developers to study, modify and extend every component of the machine learning pipeline.
Current Status
⚠️ Under Active Development
AetherMind is currently under development.
The project architecture is being implemented and integrated module by module. APIs may change as the framework evolves and compilation is still in progress while the core components are stabilized.
Current implementation includes:
- Tensor Engine
- Mathematical Engine
- Neural Network Layers
- Optimizers
- Runtime
- Computational Graph
- Compiler Infrastructure
- Dataset Pipeline
- Memory Management
- Serialization
- Quantization
- SIMD Backend
- Scheduler
- Logging
- Telemetry
- Profiling
- Testing Utilities
Framework Architecture
flowchart LR
Input["Input Data"]
Dataset["Dataset Loader"]
Tensor["Tensor Engine"]
Graph["Computational Graph"]
Compiler["Compiler"]
Optimizer["Optimizer"]
Kernel["Kernel Library"]
Runtime["Runtime Scheduler"]
Backend["CPU / SIMD / GPU"]
Output["Prediction"]
Input --> Dataset
Dataset --> Tensor
Tensor --> Graph
Graph --> Compiler
Compiler --> Optimizer
Optimizer --> Kernel
Kernel --> Runtime
Runtime --> Backend
Backend --> Output
Neural Network Flow
graph LR
Input((Input))
Dense1[Dense Layer]
Activation1[ReLU]
Dense2[Dense Layer]
Activation2[ReLU]
Dense3[Dense Layer]
Softmax[Softmax]
Prediction((Prediction))
Input --> Dense1
Dense1 --> Activation1
Activation1 --> Dense2
Dense2 --> Activation2
Activation2 --> Dense3
Dense3 --> Softmax
Softmax --> Prediction
Internal Codebase Architecture
flowchart TD
lib["lib.rs"]
lib --> backend
lib --> compiler
lib --> core
lib --> datasets
lib --> graph
lib --> io
lib --> kernel
lib --> logging
lib --> math
lib --> memory
lib --> nn
lib --> optimizer
lib --> profiler
lib --> quantization
lib --> runtime
lib --> scheduler
lib --> serialization
lib --> simd
lib --> telemetry
lib --> tensor
lib --> testing
lib --> utils
Project Structure
src/
├── backend/
├── compiler/
├── config.rs
├── core/
├── datasets/
├── error.rs
├── graph/
├── io/
├── kernel/
├── lib.rs
├── logging/
├── math/
├── memory/
├── nn/
├── optimizer/
├── prelude.rs
├── profiler/
├── quantization/
├── runtime/
├── scheduler/
├── serialization/
├── simd/
├── telemetry/
├── tensor/
├── testing/
├── utils/
└── version.rs
Module Responsibilities
| Module | Purpose |
|---|---|
| Tensor | Tensor storage and manipulation |
| Math | Numerical algorithms and linear algebra |
| NN | Neural network layers |
| Graph | Computational graph |
| Kernel | High-performance mathematical kernels |
| Runtime | Execution engine |
| Backend | CPU, SIMD and future GPU execution |
| Compiler | Graph compilation and optimization |
| Optimizer | SGD, Adam and RMSProp |
| Memory | Memory pools and allocation |
| Scheduler | Task scheduling |
| Serialization | Saving and loading models |
| Quantization | Reduced precision inference |
| Datasets | Data loading pipeline |
| Logging | Runtime logging |
| Telemetry | Runtime metrics |
| Profiler | Benchmarking and timing |
| SIMD | Architecture-specific acceleration |
| Utils | Shared helper utilities |
Development Progress
| Component | Status |
|---|---|
| Tensor Engine | 🟡 In Progress |
| Math Engine | 🟡 In Progress |
| Neural Networks | 🟡 In Progress |
| Computational Graph | 🟡 In Progress |
| Runtime | 🟡 In Progress |
| Compiler | 🟡 In Progress |
| Memory Manager | 🟡 In Progress |
| Optimizers | 🟡 In Progress |
| Serialization | 🟡 In Progress |
| Quantization | 🟡 In Progress |
| Scheduler | 🟡 In Progress |
| SIMD | 🟡 In Progress |
| Backend | 🟡 In Progress |
| Logging | 🟡 In Progress |
| Telemetry | 🟡 In Progress |
| Profiler | 🟡 In Progress |
Design Goals
- Modular architecture
- Generic tensor implementation
- Hardware abstraction
- Efficient numerical kernels
- Backend independence
- SIMD acceleration
- Future GPU support
- Extensible compiler pipeline
- Memory-efficient execution
- Strong type safety
- Maintainable codebase
- Learning-oriented implementation
Language
Primary Language
- Rust
Future interoperability may include:
- C
- CUDA
- OpenCL
- Metal
- Vulkan Compute
Code Complexity
| Area | Complexity |
|---|---|
| Tensor Engine | High |
| Computational Graph | High |
| Runtime Scheduler | High |
| Compiler | High |
| Memory Manager | High |
| Neural Networks | Medium |
| Optimizers | Medium |
| Serialization | Medium |
| Quantization | Medium |
| Dataset Pipeline | Medium |
| SIMD Backend | High |
Long-Term Vision
The long-term objective of AetherMind is to evolve into a complete machine learning framework supporting:
- Neural network training
- Neural network inference
- Automatic differentiation
- Graph optimization
- CPU execution
- SIMD acceleration
- GPU execution
- Quantized inference
- Distributed execution
- Embedded AI deployment
- Custom compiler optimizations
- Portable model serialization
Building
git clone https://github.com/seleste3580-dev/AetherMind.git
cd AetherMind
cargo build
License
Licensed under the MIT License.
AetherMind is an open-source project under active development. Contributions, bug reports, feature suggestions and discussions are welcome as the framework continues to evolve.