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arxiv: 2511.22267 · v2 · submitted 2025-11-27 · 💻 cs.AR

Aquas: Enhancing Domain Specialization through Holistic Hardware-Software Co-Optimization based on MLIR

Yuyang Zou , Youwei Xiao , Chenyun Yin , Yansong Xu , Yuhao Luo , Yitian Sun , Ruifan Xu , Renze Chen
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Yun Liang
This is my paper

Pith reviewed 2026-05-17 05:04 UTC · model grok-4.3

classification 💻 cs.AR
keywords ASIPRISC-Vhardware-software co-designMLIRcustom instructionsdomain accelerationmemory interface modelinge-graph compiler
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The pith

Aquas offers a MLIR-based co-design framework that models memory interfaces with cache awareness and uses e-graph compilation to automate custom instruction offloading for RISC-V ASIPs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that existing ASIP design flows struggle with memory bottlenecks and complex custom instructions, limiting automation in emerging domains. Aquas addresses this through a joint memory interface model and an e-graph retargetable compiler that maps and offloads instructions effectively. If the approach holds, it would enable substantial performance gains across applications while keeping hardware costs low and frequency intact. A sympathetic reader would care because this reduces reliance on manual tuning and fixed extension interfaces, making domain specialization more practical as applications grow in complexity.

Core claim

Aquas proposes a memory interface model that jointly considers interface characteristics and cache effects, along with an interface-aware synthesis flow that optimizes the input specification and generates efficient hardware. It also introduces an e-graph-based retargetable compiler with a novel matching engine for robust instruction mapping and offloading. Case studies in four domains demonstrate up to 15.61x speedup with 14.5 percent area overhead and zero frequency degradation, remaining competitive against stronger general-purpose cores and vector extensions.

What carries the argument

The memory interface model that accounts for both interface traits and cache effects, paired with an e-graph-based retargetable compiler featuring a novel matching engine for automated instruction offloading.

If this is right

  • Memory access can be optimized progressively during synthesis rather than treated as an afterthought.
  • Custom instructions with non-trivial control and memory behavior become viable for automated offloading.
  • Domain accelerators achieve high speedups while using less area than more powerful general cores.
  • The framework supports multiple diverse applications through a single retargetable compilation approach.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This style of co-optimization might shorten the iteration cycle between hardware generation and software adaptation in RISC-V ecosystems.
  • If the matching engine proves robust, similar e-graph techniques could apply to other compiler targets beyond MLIR dialects.
  • The low area overhead suggests the approach could scale to systems with multiple specialized extensions without compounding hardware costs.

Load-bearing premise

The memory interface model and e-graph compiler will generalize to new domains without needing extensive manual tuning or post-hoc fixes.

What would settle it

Applying Aquas to a fifth domain with highly irregular memory patterns and complex control logic, then measuring whether speedups remain above 5x without developer adjustments to the models or engine.

Figures

Figures reproduced from arXiv: 2511.22267 by Chenyun Yin, Renze Chen, Ruifan Xu, Yansong Xu, Yitian Sun, Youwei Xiao, Yuhao Luo, Yun Liang, Yuyang Zou.

Figure 1
Figure 1. Figure 1: Overview of the unified toolchain in Aquas. to build the cross-level flow for both the hardware synthesis and compiler support. Specifically, Aquas introduces the aquas dialect, which extends existing features of [25] with operations for fast memory access and optimization directives (Section 4.1). At the mi￾croarchitecture level, Aquas introduces a burst DMA engine based on TileLink-UH for data transfers … view at source ↗
Figure 2
Figure 2. Figure 2: Synthesis flow of gemv using Aquas. It consists of (a) CADL input, (b) MLIR parsed from CADL including aquas dialect, and (c) synthesized hardware including DMA engine, scratchpad memory, and main execution pipeline. Optimization Directives. To exploit data-level parallelism en￾abled by efficient memory access, we introduce optimization direc￾tives to guide hardware synthesis. Available directives include … view at source ↗
Figure 3
Figure 3. Figure 3: End-to-end workflow of the Aquas retargetable compiler. ❶ to ❽ correspond to the steps for compiling an application. expressions, which blocks MLIR’s loop transformations. An inter￾nal rewrite first canonicalizes the index computation (i≪2⇒i*4). Subsequently, the compiler leverages a cost model to extract a program variant that avoids non-affine (e.g., "≪") access within loops, enabling more aggressive aff… view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents Aquas, an MLIR-based holistic hardware-software co-design framework for RISC-V ASIPs. It introduces a memory interface model that jointly accounts for interface characteristics and cache effects, an interface-aware synthesis flow for hardware generation, and an e-graph-based retargetable compiler with a novel matching engine for automated instruction offloading. Case studies in four domains report up to 15.61x speedup, 14.5% area overhead, and zero frequency degradation, claiming competitiveness versus general-purpose cores and vector extensions.

Significance. If the central performance claims are substantiated by detailed ablations and reproducible methodology, the work could advance automated domain specialization by unifying memory-aware synthesis with compiler retargeting in a single MLIR infrastructure. The e-graph matching approach for irregular custom instructions represents a technically interesting direction that could reduce manual effort in ASIP flows.

major comments (2)
  1. [Evaluation / Case Studies] Evaluation section (case studies): the headline results (up to 15.61x speedup, 14.5% area, zero frequency loss) are presented without ablations that isolate the contribution of the joint memory-interface/cache model versus the novel e-graph matcher, nor any count of manually added patterns per domain. This leaves open whether the reported gains derive from the automated co-optimization framework or from domain-specific manual tuning in the synthesis and matching rules.
  2. [Memory Interface Model] Memory interface model description: the claim that the model 'jointly considers interface characteristics and cache effects' is central to addressing the stated memory bottleneck, yet the manuscript provides no quantitative comparison against prior interface-only models or sensitivity analysis on cache-effect parameters, making it impossible to verify that the model itself drives the observed interface selection improvements.
minor comments (2)
  1. [Abstract / Introduction] The abstract and introduction use 'zero frequency degradation' without specifying the synthesis tool, target process node, or timing constraints under which this holds; add a sentence clarifying the experimental setup.
  2. [Compiler Approach] Notation for the e-graph matching engine (e.g., cost functions or rewrite rules) is introduced without a compact summary table; a small table listing the novel matching heuristics would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below and describe the revisions we will incorporate to improve the manuscript.

read point-by-point responses

  1. Referee: [Evaluation / Case Studies] Evaluation section (case studies): the headline results (up to 15.61x speedup, 14.5% area, zero frequency loss) are presented without ablations that isolate the contribution of the joint memory-interface/cache model versus the novel e-graph matcher, nor any count of manually added patterns per domain. This leaves open whether the reported gains derive from the automated co-optimization framework or from domain-specific manual tuning in the synthesis and matching rules.

    Authors: We agree that explicit ablations would strengthen the evaluation by isolating component contributions. In the revised manuscript we will add ablations that separately disable the joint memory-interface/cache model and the e-graph matcher to quantify their individual effects on the reported speedups. We will also include a table reporting the number of manually added patterns per domain; these are limited to a small set of domain-specific edge cases, as the majority of instruction patterns are automatically discovered and matched by the e-graph engine. revision: yes

  2. Referee: [Memory Interface Model] Memory interface model description: the claim that the model 'jointly considers interface characteristics and cache effects' is central to addressing the stated memory bottleneck, yet the manuscript provides no quantitative comparison against prior interface-only models or sensitivity analysis on cache-effect parameters, making it impossible to verify that the model itself drives the observed interface selection improvements.

    Authors: We acknowledge the value of direct quantitative validation. The revised version will include a new subsection with comparisons of the joint model against prior interface-only models on the same benchmarks, plus sensitivity analysis varying cache-effect parameters (e.g., hit rates and latency multipliers) to show their influence on interface selection and overall performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework and results are independent of each other.

full rationale

The paper describes a hardware-software co-design framework using MLIR, a memory interface model, and an e-graph retargetable compiler, then reports empirical speedups from case studies on four domains. No equations, fitted parameters, or self-citations are shown that would make the performance numbers reduce to the inputs by construction. The claimed acceleration is presented as an outcome of applying the described components rather than a tautological renaming or load-bearing self-reference. The derivation chain for the models and compiler remains self-contained against the external benchmarks of the evaluated domains.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the memory interface model and e-graph engine are described at high level without stated assumptions or new postulated components.

pith-pipeline@v0.9.0 · 5566 in / 1178 out tokens · 26305 ms · 2026-05-17T05:04:15.795285+00:00 · methodology

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