Accelerated Scan is a focused, high-performance library for solving first-order linear recurrences (associative scans) on GPU. It provides a pure CUDA kernel (`warp`) that uses warp shuffle intrinsics, shared memory, and a hierarchical chunked algorithm to achieve state-of-the-art speed, and a Triton-based implementation (`scalar`) that leverages `tl.associative_scan` for portability. A reference PyTorch implementation (`ref`) implements the classic Blelloch tree scan for validation. The library is designed for integration into state space models and linear RNNs, where the recurrence `x[t] = gate[t] * x[t-1] + token[t]` is central. The codebase is small but exceptionally well-crafted, with clear separation of concerns, comprehensive tests, and a clean API. The CUDA kernel in particular is a standout example of GPU optimization, using warp-level primitives, shared memory, and chunking to handle sequences of arbitrary length (up to 65536) with minimal overhead.
This repository is a curated bibliography of research papers focusing on Mamba-based State Space Models applied to computer vision tasks. It contains no actual source code, data, or implementations - rather, it serves as a comprehensive survey organizes hundreds of recent arXiv papers by application domain including action recognition, medical imaging, remote sensing, video understanding, and many other CV subfields. The repository's sole content is a regularly updated README.md that lists papers with links to their arXiv versions and associated code repositories when available.