Domain-specific accelerators provide an increasingly valuable source of performance for diverse applications. Custom instructions that trigger the execution of dedicated hardware units or accelerators for common application functions become key building blocks in modern computing systems, balancing performance and cost effectiveness. RISC-V, the open and extensible instruction set architecture, is increasingly popularizing this trend. However, exploring custom instructions for an application domain remains challenging. Existing automated approaches suffer from poor reusability and limited performance. They can only identify or merge syntactically similar, scalar instruction sequences while missing semantically equivalent patterns. We present ISAMORE, an end-to-end framework for discovering reusable custom instructions from domain applications. ISAMORE encodes general applications in an e-graph by constructing a structured domain-specific language. Its core methodology, reusable instruction identification (RII), leverages e-graph anti-unification (AU) to identify semantically equivalent common patterns across diverse applications, fully unleashing the potential of custom instructions. RII employs a phase-oriented iterative process with smart heuristics to enhance the scalability when dealing with real-world codebases. Besides, RII introduces the novel pattern vectorization technique, packing common operations from scalar programs into lanes of vectorized custom instructions to exploit data-level parallelism. Moreover, RII's Pareto-optimal pattern selection balances performance gains with area overheads, guided by a profiling-based hardware-aware cost model. Evaluation demonstrates ISAMORE 's substantial performance gains, 1.12x-2.69x, over baseline approaches. We also demonstrate ISAMORE's practical potentials using various case studies, including library analysis for three application domains and hardware specialization for quantized LLM inference and post-quantum cryptography.

@inproceedings{XiaoYSZ026,
  title = {Finding Reusable Instructions via E-Graph Anti-Unification},
  author = {Youwei Xiao and Chenyun Yin and Yitian Sun and Yuyang Zou and Yun Liang},
  booktitle = {ASPLOS},
  year = {2026},
  doi = {10.1145/3779212.3790162}
}