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.
EchoTorch is a PyTorch-based research library for reservoir computing and echo state networks (ESNs). It implements multiple ESN variants, including a self-predicting ESN (SPESNCell), conceptor-based pattern manipulation with logic operations (AND, OR, NOT, PHI), a custom TimeTensor time-series wrapper, and supporting datasets such as Lorenz, Roessler, Mackey-Glass, NARMA, and MNIST. The repository is a library rather than an application: there are no web servers, CLIs, or service entry points; the primary interface is the Python package `echotorch` with submodules for neural network cells, datasets, transforms, costs, and utilities.