Hardware-Aware Optimization

• Hardware-aware optimization is a technique that integrates measured hardware metrics such as latency, energy, and throughput into the optimization process for tailored performance.
• It leverages surrogate models, reinforcement learning, and evolutionary search to jointly optimize model accuracy alongside hardware constraints.
• This approach delivers practical benefits across digital, analog, and quantum platforms by enabling Pareto-optimal trade-offs in design and deployment.

Hardware-aware optimization refers to the systematic incorporation of quantitative hardware characteristics, constraints, and performance models into the process of designing, training, compiling, and deploying software—especially machine learning models—so that the result delivers optimal or near-optimal efficiency and quality on a specific hardware platform. This paradigm bridges the gap between abstract algorithmic choices and concrete execution on heterogeneous or specialized hardware, including CPUs, GPUs, FPGAs, ASICs, accelerators, quantum processors, and analog or neuromorphic substrates. Hardware-aware optimization integrates metrics such as latency, energy consumption, memory footprint, throughput, and gate fidelity into the optimization process, leveraging hardware resource models, real measurement, or analytic/surrogate prediction, and typically results in delivering superior trade-offs along relevant Pareto fronts for practical deployment.

1. Principles and Frameworks of Hardware-Aware Optimization

Hardware-aware optimization mandates the explicit modeling of hardware constraints and efficiency metrics in the optimization loop. This may occur at the level of hyperparameter or neural architecture search, code compilation, or circuit mapping.

• Model-based Optimization: Hardware-aware sequential model-based optimization (SMBO) uses surrogate models to predict not only accuracy but also hardware metrics such as power, memory, or latency (for example, HyperPower’s linear models for power and memory). Constraints are imposed via indicator functions in acquisition objectives, e.g., optimizing only over configurations satisfying hardware budgets (Marculescu et al., 2018).
• Quantitative Metrics: Heuristics or regression models estimate "complexity," "priority" (uncertainty), or hardware execution time as in SHADHO’s complexity formula

$$C(s_i) = \begin{cases} 2 + \lVert b-a \rVert & \text{if } s \text{ is continuous} \ 2 - \frac{1}{|s_i|} & \text{if } s \text{ is discrete} \end{cases}$$

which sums across search spaces, and direct hardware-prioritized scheduling (Kinnison et al., 2017). Others, such as NeuralPower, use polynomial regression for layer latency/power prediction.

• Hierarchical Optimization: Several systems, e.g., HAO (&&&2&&&), use integer programming to co-optimize hardware-software solutions (e.g., FPGA accelerator allocation, quantization, and architecture) under explicit resource and latency constraints, forming Pareto frontiers for accuracy and hardware efficiency.
• Co-Optimization: Frameworks integrate multi-level algorithm-hardware co-design: network topology, quantization, pruning, and even microarchitectural choices are optimized in a single loop (Marculescu et al., 2018, Dong et al., 2021, Wang et al., 5 Nov 2024).

2. Incorporation of Hardware Metrics and Constraints

Key to hardware-aware optimization is the use of hardware-specific performance models or real measurements:

• Predictive Models: Surrogate models (regression, neural, or polynomial) predict energy, latency, or memory, enabling constraint enforcement at the search stage (Marculescu et al., 2018, Sukthanker et al., 16 May 2024).
• Direct Hardware Feedback: HAQ directly integrates latency and energy feedback from hardware simulators into its reinforcement learning loop, allowing the RL agent to adapt quantization policies across layers for edge, cloud, or custom accelerators, rather than relying on proxy metrics like FLOPs (Wang et al., 2018). HW-GPT-Bench uses calibrated quantile regression and heteroscedastic noise modeling for multi-device latency and energy prediction on LLMs (Sukthanker et al., 16 May 2024).
• Profiling and Runtime Analysis: SwizzlePerf demonstrates that LLM-based optimizers, when provided with cache hit rates and explicit topology (e.g., number of XCDs on GPUs), can outperform heuristic and search-based kernel optimization, matching expert human engineering efforts for GPU scheduling (Tschand et al., 27 Aug 2025). Vortex (Zhou et al., 2 Sep 2024) uses detailed hardware spec (registers, cache, memory bandwidth, instruction units) for sample-free compilation optimization.

The integration of hardware metrics is not limited to digital systems. Heim statically incorporates hardware error models (bit flip probabilities in emerging nonvolatile memories) to determine minimum reliable hypervector size in hyperdimensional computing (Pu et al., 2023), and Shem applies gradient-based optimization over nonlinear ODEs including device mismatch and stochastic noise for analog systems (Wang et al., 5 Nov 2024). On quantum hardware, HAQA and distributed QAOA frameworks introduce hardware-aware mapping and algorithm partitioning strategies that factor fidelity and error rates directly into region selection and scheduling (Sun et al., 23 Apr 2025, Chen et al., 24 Jul 2024).

3. Multi-Objective and Pareto-Optimal Design

Most hardware-aware optimization methods are formulated as multi-objective optimization (MOO) or constrained optimization problems:

• Explicit Optimization Objectives: SONATA, HURRICANE, and multiple NAS systems seek to minimize error, latency, and energy jointly by searching the space of possible architectures under platform-specific constraints. The objectives are formalized as:

$$\min\{\text{Err(a)}, \text{Lat(a)}, \text{Ergy(a)}\}$$

subject to explicit design or resource constraints.

• Pareto Efficiency: Pareto sorting and dominance are used to select non-dominated solutions in multi-objective neural architecture search (e.g., NSGA-II in SONATA), quantified through hypervolume and dominance ratio metrics. SONATA reports up to 93.6% Pareto dominance over static baselines—demonstrating the efficiency of self-adaptive evolutionary operators (Bouzidi et al., 20 Feb 2024).
• Trade-Off Surfaces: HW-GPT-Bench provides simulatable Pareto fronts for model perplexity versus latency, energy, and memory via fast surrogate models, enabling offline evaluation of NAS algorithms (Sukthanker et al., 16 May 2024). Hardware-aware pruning (HALP) solves a global knapsack optimization for maximum accuracy under latency budgets, using augmented solvers and saliency lookup tables (Shen et al., 2021).

4. Strategies and Algorithms for Hardware-Aware Search

Optimization algorithms in this domain are characterized by:

• Reinforcement Learning and Evolutionary Search: RL agents are employed for quantization and NAS (e.g., DDPG in HAQ), with rewards coupled to hardware metrics (Wang et al., 2018). Adaptive evolutionary approaches (SONATA) use surrogate models and RL policy gradients to focus mutation/crossover on impactful design parameters (Bouzidi et al., 20 Feb 2024).
• Surrogate Prediction and Predictor-Assisted Search: Predictor-assisted concurrent search with NSGA-II (as in concurrent NAS) reduces expensive validation costs during sub-network extraction from supernets by training weak regressors and using iterative search (Sarah et al., 2022). HW-GPT-Bench uses sandwich training weight-sharing and regression models for rapid evaluation.
• Integer Programming and Knapsack Formulations: HAO and HALP utilize integer programming to reduce computational search cost in hardware-aware co-design and latency-constrained pruning (Dong et al., 2021, Shen et al., 2021).
• Hardware-Aware Sampling and Search Pruning: Validity-driven initialization in auto-tuning frameworks employs neighborhood sampling to bias early exploration toward plausible, executable operator configurations, reducing hardware trials by over half (Rieber et al., 2022). HAQA for quantum mapping applies community-based graph partitioning and fidelity-based region selection to reduce mapping complexity from global to quadratic in problem size, with major runtime acceleration (Sun et al., 23 Apr 2025).

5. Specialized Hardware Domains and Co-Optimization

The field encompasses diverse hardware targets and optimization strategies:

• DNN Accelerators and Sparsity: HASS jointly optimizes for both weight and activation sparsity with dataflow scheduling and resource allocation in FPGA accelerators, achieving up to 4.2× throughput gain (Yu et al., 5 Jun 2024).
• Analog and Emerging Substrates: Shem extends hardware-aware optimization to analog and mixed-signal platforms, enabling gradient-based tuning of ODE-based models (edge-detecting CNNs, oscillator-based pattern recognition) subject to noise, mismatch, and discrete constraints through differentiable programming (Wang et al., 5 Nov 2024). Heim’s static analysis yields minimal resource configurations robust to noise in analog CAMs and multibit ReRAM (Pu et al., 2023).
• Quantum Hardware: Hardware-guided qubit mapping in HAQA incorporates fidelity metrics into region selection, producing polynomial acceleration and up to 238.28% fidelity improvement (Sun et al., 23 Apr 2025). Distributed QAOA frameworks decompose computation and allocate subproblems to high-fidelity QPU regions based on measured or predicted error rates, maximizing global accuracy and resource utility (Chen et al., 24 Jul 2024).
• Compilers, Kernels, and Scheduling: Vortex uses bidirectional compilation (hierarchical program decomposition/top-down and hardware-guided kernel construction/bottom-up) to generate kernels adapted to hardware resources, delivering >170× compilation time reduction over sample-driven compilers (Zhou et al., 2 Sep 2024). SwizzlePerf demonstrates adaptive GPU kernel scheduling using LLMs and hardware profiler data, replicating expert human optimizations with up to 2.06× speedup and 70% L2 cache hit rate improvement (Tschand et al., 27 Aug 2025).

6. Impact, Challenges, and Future Perspectives

Hardware-aware optimization has led to substantial empirical improvements in throughput, latency, energy, and deployment efficiency across platforms and model domains. Noteworthy reported metrics include double throughput for SHADHO over FCFS schedulers (Kinnison et al., 2017), ~2× speedup/energy gain for HAQ over fixed quantization (Wang et al., 2018), 1.3–4.2× DNN accelerator gains with HASS (Yu et al., 5 Jun 2024), and up to 632.76× acceleration for quantum circuit placement via HAQA (Sun et al., 23 Apr 2025).

Despite progress, several open challenges are identified:

• Robust generalization of hardware performance models to increasingly nonlinear and heterogeneous architectures (Marculescu et al., 2018).
• Accurate and scalable hardware cost estimation for multi-device and multi-objective setups (Benmeziane et al., 2021).
• Integration of NAS with advanced compression (quantization, pruning, mixed-precision) (Benmeziane et al., 2021).
• Hardware-software co-design at all levels, especially as analog and quantum platforms emerge with fundamentally different error models and computation paradigms (Wang et al., 5 Nov 2024, Pu et al., 2023, Sun et al., 23 Apr 2025).
• Usability and transferability of hardware-aware optimizations across rapidly evolving device landscapes.

Current trends suggest further unification of search, surrogate prediction, and adaptive optimization mechanisms—extensible across both digital and non-digital hardware. Open-source frameworks (e.g., HASS, HW-GPT-Bench, Shem) accelerate adoption and reproducibility, while advances in compiler co-design and online profiling (Vortex, SwizzlePerf) offer routes toward fully autonomous hardware-software co-optimization.

7. Summary Table: Representative Approaches

Framework/Domain	Optimization Method	Hardware Awareness Mechanism
SHADHO (Kinnison et al., 2017)	Heuristic scheduling (complexity, priority)	Worker compute class, task–resource heuristics
HAQ (Wang et al., 2018)	RL-based mixed-precision quantization	Direct device latency/energy feedback
HURRICANE (Zhang et al., 2019)	Two-stage hardware-aware NAS	Operator benchmarking, layer grouping
HALP (Shen et al., 2021)	Augmented knapsack pruning	Latency lookup tables, filter grouping
HASS (Yu et al., 5 Jun 2024)	Bayesian multi-objective sparsity/hardware co-optimization	Layerwise pruning integrated with hardware DSE
Shem (Wang et al., 5 Nov 2024)	Differentiable ODE simulation	Analog device/model parameters, noise/mismatch models
HAQA (Sun et al., 23 Apr 2025)	Community-based region partition; hardware-prioritized mapping	Connectivity/fidelity-based region selection
SwizzlePerf (Tschand et al., 27 Aug 2025)	LLM-guided, profile-informed kernel scheduling	Structured hardware and profiler input to LLM

This table lists a subset of state-of-the-art hardware-aware optimization systems, highlighting the diversity of strategies for integrating hardware characteristics into the optimization process.