AIKA (Artificial Intelligence for Knowledge Acquisition) is an experimental AI framework designed to explore alternative approaches to deep learning.
Unlike conventional frameworks like PyTorch, AIKA focuses on event-driven computation, dynamic object instantiation, and type-hierarchy-based graph representation.
Key Features:
- Event-Driven Processing: AIKA processes activations asynchronously using an event queue, enabling dynamic and sparse computation.
- Dynamic Object Graph: Unlike static vector/matrix-based architectures, AIKA instantiates network elements at runtime, making the topology adaptable.
- Type-Based Model Representation: AIKA’s functional graph is linked to dynamically created objects, allowing more flexible neural network architectures.
- Python & C++ Hybrid: AIKA integrates Python for ease of use and C++ for high-performance execution.
To get started, check out the Installation Guide and Example Use Cases.