High-Performance Emulation Methods

• High-performance emulation methods are a set of algorithmic and hardware strategies designed to replicate the performance of specific systems while bypassing cycle-accurate details.
• They achieve significant speedups—up to 100× in some cases—by leveraging operator fusion, adaptive precision, and hardware acceleration techniques such as FPGAs and INT8 matrix engines.
• Transparent integration with host systems through dynamic binary translation and memory emulation enables practical validation and scalability across domains like quantum simulation and SoC verification.

High-performance emulation methods encompass a set of algorithmic, architectural, and systems-level strategies designed to replicate the functional and performance characteristics of one class of hardware, memory, or computational system on a platform with fundamentally different operating constraints. Unlike low-level simulation, emulation methods are explicitly engineered for orders-of-magnitude speedup, enabling realistic workloads, large system sizes, or high-fidelity modeling. Recent work spans domains including memory and storage hierarchies, quantum and classical hardware, numerical linear algebra, SoC verification, network protocol development, and computation-intensive physical models. Methods leverage hardware acceleration (FPGAs, AI matrix engines), algorithmic shortcutting, and precision management to maximize throughput while retaining controlled accuracy or system transparency.

1. Algorithmic Principles and Core Techniques

High-performance emulation exploits abstraction and algorithm substitution to side-step the strict stepwise fidelity of cycle- or gate-level simulation. Key principles are:

• Operator Fusion and Shortcutting: For quantum algorithms, emulators replace gate-by-gate simulation with classical analogues (e.g., direct FFT for quantum Fourier transform, permutation or fused kernels for arithmetic) (Häner et al., 2016). For matrix multiplications, integer-based emulation circumvents hardware FP64/FP32 bottlenecks by operating on quantized or CRT-split integer panels and reconstructing high-precision outputs (Uchino et al., 6 Aug 2025, Uchino et al., 9 Dec 2025).
• Approximate and Adaptive Precision: Emulators implement tunable-precision workflows where the bitwidth or number of computation slices (e.g., Ozaki-split in GEMM emulation) can be tailored to application-level tolerance and operator condition (Liu et al., 28 Mar 2025, Uchino et al., 6 Aug 2025).
• Structural Abstraction: Large-scale memory is emulated by logically composing small, distributed SRAMs, interconnected to present a single, flat global address space with performance cost bounded by low-diameter switch topologies (Hanlon, 2012).
• Matrix Product State (MPS) and Tensor-Network Decompositions: For quantum many-body problems, emulators use MPS or MPO factorizations to reduce memory scaling from $O(2^N)$ (state vector) to $O(N\chi^2)$ or better, supporting $N \gg 30$ qubits under controlled approximation (Bidzhiev et al., 10 Oct 2025).

2. Hardware-Accelerated Emulation Architectures

Architectural choices are dictated by target domain and performance requirements:

• FPGA-Based Prototyping: Hardware emulators such as METICULOUS (Hirofuchi et al., 2023), HeteroBox (Chen et al., 26 Feb 2025), and Makinote (Perdomo et al., 31 Jan 2024) implement per-request manipulation of latency, bandwidth, and error rates, with full transparency to host OS and privileged code. Platform shells (e.g., Makinote's YAML-configured FPGA shell) abstract device specifics, facilitating rapid porting to multi-FPGA clusters.
• Memory and Storage Emulation: Main memory and NVM characteristics—including region-specific access times, bandwidth caps, bit-flip injection—are emulated via token-bucket controllers, hardware FIFOs, and MMIO configuration (Hirofuchi et al., 2023, Chen et al., 26 Feb 2025, Koshiba et al., 2019). FPGA-based systems can achieve hundreds of MB/s throughput per region with deterministic sub-microsecond precision.
• Matrix Engine Utilization: INT8 matrix engines are leveraged via CRT- and Ozaki-based schemes to accelerate high-precision GEMM (both real and complex) on AI-focused hardware, yielding $3\times$–$6\times$ speedups over legacy FP64/FP32 kernels for sufficiently large matrices (Uchino et al., 6 Aug 2025, Uchino et al., 9 Dec 2025).
• Cycle-Accurate RTL Emulation: Scale-down co-emulation (e.g., ZynqParrot (Ruelas-Petrisko et al., 24 Sep 2025)) partitions large SoC designs into independently prototyped, cycle-accurate subsystems, enforcing strict non-interference via clock gating, SB-FIFOs, and software-controlled event queues.

3. Numerical, Fidelity, and Performance Considerations

Emulation methods address non-ideal effects and approximation errors through:

• Controlled Truncation and Uniqueness Bounds: Integer-based GEMM emulations enforce uniqueness criteria on modular product bounds (e.g., $2\sum_{h}|a'_{ih}||b'_{hj}| < P$) and select moduli to match application accuracy (Uchino et al., 6 Aug 2025, Uchino et al., 9 Dec 2025). Numerical accuracy can be dialed via additional CRT moduli or Ozaki slices, with exponential reduction in error per increment in $N$ or $s$ (Uchino et al., 6 Aug 2025, Liu et al., 28 Mar 2025).
• Error Propagation in Tensor Networks: In MPS-based quantum emulation, SVD truncation error accumulates linearly with step count and bond truncations, but practical observed errors are often smaller due to error cancellation (Bidzhiev et al., 10 Oct 2025).
• Empirical and Theoretical Benchmarks: FPGA-based emulators demonstrate $>10\times$–$100\times$ speedups vs. traditional simulation (e.g., hardware design verification (Cong et al., 2016), quantum emulators vs. QuTiP (Bidzhiev et al., 10 Oct 2025), GEMM emulation on GH200 (Uchino et al., 6 Aug 2025)), while maintaining controlled error bounds or full signal visibility.

4. Systems Integration and Transparency

A defining feature of high-performance emulators is the ability to present emulated resources transparently to complex host stacks:

• Full System Emulation and DBT: Advanced dynamic binary translators eschew intermediate representations (IR) when feasible, enabling direct guest-host binary translation with up to $35\times$ speedup over TCG-based engines (Parker, 6 Jan 2025). Automatically-learned translation rules with coordination elimination offer up to $1.36\times$ average speedup in QEMU system-mode for SPEC06 workloads (Jiang et al., 15 Feb 2024).
• Transparent Memory Region Emulation: METICULOUS (Hirofuchi et al., 2023) and HeteroBox (Chen et al., 26 Feb 2025) assign physical address regions to hardware-backed emulation slices, exposing performance-characterized memory as standard devices (NVDIMM, NUMA, /dev/pmem) with runtime-configurable parameters, visible even to operating system kernels and hypervisors.
• Host-Target Protocols and Syscall Emulation: FASE (Meng et al., 10 Sep 2025) introduces a minimal hardware interface plus an efficient Host-Target Protocol for syscall emulation, supporting end-to-end processor performance validation of complex multi-threaded benchmarks directly on FPGAs, while registering $<1\%$ validation error and $>2000\times$ speedup over simulation.

5. Domain-Specific Emulation Strategies

Distinct domains demand domain-adapted emulation strategies:

Domain	Core Emulation Approach	Notable Works
Quantum simulation	State-vector, MPS/TDVP, operator fusion	(Bidzhiev et al., 10 Oct 2025, Häner et al., 2016, Duy et al., 8 Oct 2025)
Memory systems/NVM	HW region slicing, per-access time/bandwidth	(Hirofuchi et al., 2023, Chen et al., 26 Feb 2025, Koshiba et al., 2019)
Linear algebra	CRT/Ozaki integer decomposition, INT8 engines	(Uchino et al., 6 Aug 2025, Uchino et al., 9 Dec 2025, Liu et al., 28 Mar 2025)
Hardware/SoC development	FPGA shell, cycle-accurate, scale-down	(Perdomo et al., 31 Jan 2024, Ruelas-Petrisko et al., 24 Sep 2025, Cong et al., 2016)
Network emulation	Kernel qdisc, µs-trace-file accuracy	(Ottens et al., 30 Oct 2025)
Binary translation	Direct translation, learned rule DBT	(Parker, 6 Jan 2025, Jiang et al., 15 Feb 2024)
Power/grid control	LMI-certified inertia emulation, MRC	(Zhang et al., 2017)

6. Practical Impact, Limitations, and Best Practices

• Throughput and Efficiency: Modern INT8 matrix engines now achieve $3\times$–$6\times$ higher throughput than legacy FP64 for matrix multiplication; FPGA-accelerated emulation delivers $>70\times$ speedup over simulation in SoC tasks (Uchino et al., 6 Aug 2025, Cong et al., 2016, Uchino et al., 9 Dec 2025).
• Transparency: Region-based memory emulation requires no application/kernel source modifications and supports run-time reconfiguration (Hirofuchi et al., 2023, Chen et al., 26 Feb 2025). Advanced DBT engines can maintain QEMU’s wide ISA/guest support while targeting common pairs for direct translation acceleration (Parker, 6 Jan 2025).
• Scalability: MPS/TDVP emulation enables 1D Rydberg dynamics for $N>100$ qubits; full state-vector methods max at $N\sim27$ on current GPUs (Bidzhiev et al., 10 Oct 2025). FPGA clusters (e.g., Makinote (Perdomo et al., 31 Jan 2024)) achieve linear speedup for large-scale HPC emulation tasks.
• Domain Constraints: CRT-based GEMM methods require uniqueness bounds and fail on memory-bound/small-shape regimes. Some emulation approaches—e.g., FASE—do not model peripheral/IO device behavior.
• Best Practices: Dynamically tune precision or region configuration based on workload sensitivity (Liu et al., 28 Mar 2025, Uchino et al., 6 Aug 2025). Design hardware wrappers to allow cycle-accurate gating under backpressure (Ruelas-Petrisko et al., 24 Sep 2025). Validate approximate/backend results against exact emulators or reference runs whenever possible (Bidzhiev et al., 10 Oct 2025).

7. Outlook and Ongoing Challenges

Emerging directions include:

• Automated Precision Control: Integration of runtime error detection and adaptive precision switching within the emulator to optimize the performance–accuracy tradeoff (Liu et al., 28 Mar 2025).
• On-the-fly Topology and Geometry Emulation: Support for dynamic memory geometry alterations, programmable switches, and power-gating/cold-start behaviors in memory emulation platforms (Hirofuchi et al., 2023, Chen et al., 26 Feb 2025).
• Seamless ML/Gradient Integration: GPU emulation backends exposing full autodiff for ML research, including planned support for MPS differentiation (Bidzhiev et al., 10 Oct 2025).
• Scaling to Multi-System and Multi-Physical-Environments: Extending host–target protocols and co-simulation frameworks to multi-FPGA, multi-host, or cross-domain testbeds (Perdomo et al., 31 Jan 2024, Ruelas-Petrisko et al., 24 Sep 2025).

While high-performance emulation now enables comprehensive, realistic validation across diverse technical domains, continued advances will depend on further reductions in configuration/engineering overhead, cross-layer adaptability, and quantitative guarantees on the relationship between emulation parameters and system fidelity.