APyTypes: Algorithmic Data Types in Python for Efficient Simulation of Finite Word-Length Effects

A new Python library, APyTypes, suitable for simulating and exploring finite word-length effects is presented. The library supports configurable bit-accurate fixed- and floatingpoint types of both scalars and multidimensional arrays and uses a C++ backend to accelerate runtime performance. The underlying design principles of the library are introduced and examples show how it can be used. We argue that APyTypes have significant advantages over existing arithmetic libraries, especially from a hardware design perspective. Finally, some directions for further work are outlined.