Linblocks

Linblocks are a modular, block-based representation of linear transformations used in data processing and machine learning pipelines. A linblock is a self-contained unit that encapsulates a linear operation, its parameters, and minimal metadata. Linblocks are designed to be linked into pipelines where the output of one block feeds the input of the next, enabling streaming processing with predictable latency.

Each linblock exposes a simple interface comprising initialization, a process step that applies the linear operation to a batch of inputs, and a reset. Blocks can be connected into directed graphs, allowing fan-out and parallel execution while preserving a clear data flow. They are often serialized with a descriptor that records the block type, parameters, and topology.

Common linblocks implement linear operations such as scaling, translation, affine transforms, and projections. More advanced blocks can represent chains of transformations or parameterized operators and can be combined with non-linear components in hybrid pipelines.

Implementation and portability: Linblocks are implemented in multiple languages and designed to be language-agnostic. Lightweight descriptors enable cross-language interchange and deployment in real-time systems.

Applications include streaming feature extraction, signal processing, sensor fusion, and preprocessing for machine learning. Linblocks emphasize modularity, testability, and debuggability, at the cost of added descriptor overhead and potential latency if not optimized.

Example: a scaling linblock multiplies input vectors by a factor, followed by an offset linblock that adds a bias. A simple pipeline can chain these blocks to produce a normalized feature vector.

See also: data flow programming; block-based architectures; linear algebra; streaming computation.