Decision Tree Generation for Decoding Irregular Instructions
Katsumi Okuda, Haruhiko Takeyama · 2016
Instruction set simulators (ISS) are indispensable tools for the development of new architectures and embedded software. One essential part of any ISS is its instruction decoder. Since manual implementation of an instruction decoder for a complex instruction set is tedious and error-prone, automatic generation of an instruction decoder is required. However, as a result of the increasing irregularity of instruction encoding because of the incremental addition of instructions, generating efficient instruction decoders is complicated. In this paper, we propose a generation algorithm of a decision tree for decoding irregular instructions. Our algorithm can generate decision trees by using not only significant bits of opcode patterns but also exclusion conditions in decoding entries. Our results on ARMv7, Thumb-2, MIPS64, RH850, and TriCore show that our algorithm generates efficient instruction decoders in terms of both depth and memory consumption regardless of whether the target instruction set is irregular or not.