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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.22267 |
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| _version_ | 1866911602335285248 |
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| author | Zou, Yuyang Xiao, Youwei Yin, Chenyun Xu, Yansong Luo, Yuhao Sun, Yitian Xu, Ruifan Chen, Renze Liang, Yun |
| author_facet | Zou, Yuyang Xiao, Youwei Yin, Chenyun Xu, Yansong Luo, Yuhao Sun, Yitian Xu, Ruifan Chen, Renze Liang, Yun |
| contents | Application-Specific Instruction-Set Processors (ASIPs) built on the RISC-V architecture offer specialization opportunities for various applications. Existing frameworks are largely designed around fixed instruction extension interfaces and rely on manual software adaptation. However, as emerging domains scale up in complexity, two major challenges arise. First, memory access remains a primary bottleneck as existing design flows lack architectural awareness of memory interfaces, leading to suboptimal interface selection and orchestration. Second, the semantic complexity of custom instruction extensions, characterized by non-trivial control logic and irregular memory behaviors, hinders the ability of conventional compilers to perform automated and comprehensive offloading.
We present Aquas, a holistic hardware-software co-design framework built upon MLIR. Aquas proposes a memory interface model that jointly considers interface characteristics and cache effects, along with an interface-aware synthesis flow guided by this model that progressively optimizes the input specification and generates efficient hardware implementations. We also propose an e-graph-based retargetable compiler approach with a novel matching engine for efficient instruction mapping and offloading, enabling robust and effective utilization of custom instruction capabilities. Case studies across four diverse domains show that Aquas delivers substantial acceleration, achieving up to 15.61x speedup with 14.5% area overhead and zero frequency degradation, proving highly competitive in domain acceleration against more powerful general-purpose cores and vector extensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aquas: Enhancing Domain Specialization through Holistic Hardware-Software Co-Optimization based on MLIR Zou, Yuyang Xiao, Youwei Yin, Chenyun Xu, Yansong Luo, Yuhao Sun, Yitian Xu, Ruifan Chen, Renze Liang, Yun Hardware Architecture Application-Specific Instruction-Set Processors (ASIPs) built on the RISC-V architecture offer specialization opportunities for various applications. Existing frameworks are largely designed around fixed instruction extension interfaces and rely on manual software adaptation. However, as emerging domains scale up in complexity, two major challenges arise. First, memory access remains a primary bottleneck as existing design flows lack architectural awareness of memory interfaces, leading to suboptimal interface selection and orchestration. Second, the semantic complexity of custom instruction extensions, characterized by non-trivial control logic and irregular memory behaviors, hinders the ability of conventional compilers to perform automated and comprehensive offloading. We present Aquas, a holistic hardware-software co-design framework built upon MLIR. Aquas proposes a memory interface model that jointly considers interface characteristics and cache effects, along with an interface-aware synthesis flow guided by this model that progressively optimizes the input specification and generates efficient hardware implementations. We also propose an e-graph-based retargetable compiler approach with a novel matching engine for efficient instruction mapping and offloading, enabling robust and effective utilization of custom instruction capabilities. Case studies across four diverse domains show that Aquas delivers substantial acceleration, achieving up to 15.61x speedup with 14.5% area overhead and zero frequency degradation, proving highly competitive in domain acceleration against more powerful general-purpose cores and vector extensions. |
| title | Aquas: Enhancing Domain Specialization through Holistic Hardware-Software Co-Optimization based on MLIR |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2511.22267 |