HW-Aware NAS-Inspired Feature Selection

Updated 3 September 2025

Hardware-aware NAS-inspired feature selection is a technique that integrates neural architecture search with feature selection to meet both accuracy and hardware cost requirements.
It expands the standard feature space by incorporating device metrics such as latency, energy, and memory bandwidth to enable efficient deployment on resource-constrained devices.
Multi-objective optimization using surrogate models and diverse search algorithms facilitates robust trade-offs between predictive performance and hardware limitations.

Hardware-aware NAS-inspired feature selection refers to the class of techniques that apply insights, principles, and methodologies from hardware-aware Neural Architecture Search (HW-NAS) to the challenge of selecting input features or internal signals that satisfy both predictive performance and explicit hardware constraints. Unlike classical feature selection that prioritizes only predictive relevance, this paradigm incorporates device-specific metrics such as latency, energy, memory bandwidth, and inference time—either directly in the search process or as optimization constraints—enabling deployment of machine learning pipelines in resource-constrained environments.

1. Defining Hardware-Aware Feature Selection and Its NAS Foundations

Hardware-aware feature selection seeks to identify subsets of features (inputs, intermediate signals, or transformations) that maximize a model’s accuracy or error prediction capabilities while conforming to system-level constraints such as timing, energy consumption, or hardware resource usage. This is motivated by the dual-objective of HW-NAS: simultaneously optimizing neural architectures for both computational performance (e.g., F1 score, classification accuracy) and hardware efficiency (e.g., inference latency, power/energy) (Benmeziane et al., 2021).

Applying HW-NAS methodology involves:

Formulating search spaces that enumerate not only feature candidates but also their processing costs,
Employing search or optimization algorithms (RL, evolutionary, sequential, differentiable) capable of optimizing for both accuracy and hardware metrics, and
Integrating hardware cost predictors, direct measurements, or analytical models into the evaluation or rejection criteria, as is common for HW-NAS frameworks (Jiang et al., 2019, Srinivas et al., 2019, Zhang et al., 2019, Li et al., 2021).

2. Space Expansion and Scheduling-Aware Feature Selection

A paradigmatic example is found in scheduling-aware feature selection for error prediction in approximate computing systems (Nikkhah et al., 2018). This approach expands the feature space to include both accelerator inputs and intermediate signals (results generated at different cycles of hardware execution), dramatically enhancing the discriminative power for error prediction. Key innovations include:

Scheduling-awareness: Only features available within the system’s prediction time window are considered. Formally, for a feature available at cycle $i$ , the number of selectable features is bounded by:

$AFL_i = \min \left( \frac{T_{const} - i + 1}{T_{MAC}},\ FLUB,E \right)$

Resource-constrained selection: Feature selection algorithms (filter, wrapper, particle swarm, quantum genetic) are augmented with constraints reflecting both timing ( $T_{const}$ ) and energy (via total MAC operations).
Flexible accuracy/timing tradeoffs: Allowing prediction extension beyond accelerator latency enables selection of “later” features, yielding up to 83% improvement in F1 score. Benchmarks show up to 24.5× gain in F1 for linear predictors, with energy/latency bounds respected.

This strategy demonstrates that including intermediate hardware-derived signals, combined with real timing/energy budgeting, leads to robust predictors deployable under strict resource requirements.

3. Multi-Objective Formulations and Hardware Metrics

A central principle is formalizing feature selection as a multi-objective optimization:

$\max_{S \subset \mathcal{F}} \ f_{acc}(S), \quad \text{subject to} \quad g_i(S) \leq T_i \ \forall i \in I$

where $S$ is a feature subset, $f_{acc}$ is the task metric (e.g., accuracy, F1), and $g_i$ are hardware cost estimators (e.g., timing, energy, memory) (Benmeziane et al., 2021).

Several methodologies and search strategies from HW-NAS apply directly:

Reward shaping that immediately rejects or penalizes feature sets violating hardware limits (Jiang et al., 2019, Zhang et al., 2019).
Differentiable relaxations for soft and trainable selection, enabling end-to-end optimization (e.g., Gumbel-Softmax masks for features/block choices) (Srinivas et al., 2019).
Surrogate models for hardware cost, including regression trees, MLPs, and meta-learned predictors that generalize to previously unseen devices (Lee et al., 2021).
Hardware cost diversity objectives that ensure exploration across a wide spectrum of device-specific constraints, ensuring coverage of regimes (low/high-latency, high/low-energy features) (Sinha et al., 15 Apr 2024).

These mechanisms guarantee that selection is not purely guided by statistical or information-theoretic feature merit, but also grounded in hardware feasibility and deployment realism.

4. Proxy and Surrogate Metrics for Rapid Evaluation

The cost of evaluating feature sets via end-to-end training or hardware measurement can be prohibitive. HW-NAS-inspired methods mitigate this via proxy and surrogate metrics:

Training-free accuracy proxies (e.g., NASWOT, Synflow, SkipScore) are used as stand-ins for full accuracy assessment (Capuano et al., 1 Apr 2025).
Representation similarity metrics, such as Representation Mutual Information (RMI), are leveraged as rapidly computable estimators of performance. For features, one can use:

$\phi(\alpha^*, \alpha) = \sum_{i=1}^L I(X_i^*, X_i)$

where $X_i^*$ , $X_i$ are feature representations at layer $i$ for a reference and candidate, and $I(\cdot, \cdot)$ denotes mutual information, typically computed via the Frobenius norm or related estimates (Sinha et al., 2023, Sinha et al., 15 Apr 2024).

Device-aware surrogate models: Predictors are meta-learned across device embeddings, allowing near-zero-shot prediction of hardware metrics like latency on new platforms after a handful of direct measurements (Lee et al., 2021). This accelerates both feature selection and NAS, greatly reducing search time and resource usage.

5. Search Algorithms: Adaptation and Exploration

HW-NAS-inspired search strategies adopted for feature selection include:

Evolutionary algorithms with self-adaptive operators (Bouzidi et al., 20 Feb 2024),
Reinforcement learning controllers pre-trained on synthetic device distributions and fine-tuned to each real target via a few direct measurements (Capuano et al., 1 Apr 2025),
Multi-objective evolutionary optimization (e.g., NSGA-II) for balancing performance, hardware cost, and cost diversity in feature subsets (Sinha et al., 15 Apr 2024),
Population-based methods that ensure diverse Pareto fronts, thus supplying feature sets tuned for a range of accuracy/latency/energy trade-offs (Sarah et al., 2022).

A distinguishing element is the joint optimization of feature merit and hardware cost, often with “soft” penalty terms—for example, subtracting from the fitness function in proportion to violation of device resource budgets (Sinha et al., 2023).

6. Practical Applications and Benchmarking Insights

Hardware-aware feature selection has proven advantageous in real-world approximate computing systems, edge devices, and embedded AI platforms where resource constraints are severe. For example:

In error prediction, leveraging both inputs and hardware-generated intermediate results under strict energy/timing constraints achieves up to 100% large error coverage with F1 improvements of 2.3×–24.5× for conventional benchmarks (Nikkhah et al., 2018).
When feature extraction is computationally heterogeneous (e.g., multimodal or sensor fusion applications), hardware-aware NAS-inspired selection ensures that the chosen representations are not only discriminative but feasible for the deployment device’s latency and energy envelope (Ghebriout et al., 2023).
NAS benchmarks now include device-specific measurements, enabling reproducible and accessible evaluation of feature selection under hardware constraints (Li et al., 2021, Ahmad et al., 9 Apr 2024).
Multi-objective and diversity-promoting search ensures rich solution sets for practitioners, accommodating a spectrum from lowest-latency to highest-accuracy feature sets.

7. Challenges, Limitations, and Future Perspectives

Despite significant advancements, several limitations and open challenges remain in hardware-aware NAS-inspired feature selection:

Search space size: The combinatorial explosion of possible feature subsets, especially when allowing feature transformations or intermediate hardware signals, necessitates efficient and scalable search algorithms.
Accurate cost estimation: Hardware cost predictors must generalize robustly across diverse, heterogeneous deployment environments; transferability and adaptation across platforms is non-trivial (Lee et al., 2021, Capuano et al., 1 Apr 2025).
Balancing multiple conflicting objectives: Tuning scalarizations or Pareto trade-offs between accuracy and cost, and maintaining solution diversity, continues to pose non-trivial design and practical calibration issues (Sinha et al., 15 Apr 2024).
Integration with learned feature extraction: Extending NAS-inspired feature selection to pipelines where features themselves are learned (e.g., deep frontend encoders) requires co-design of end-to-end cost-aware models with flexible feature selection layers.

A plausible implication is that future research will unify hardware-aware NAS and feature selection into integrated, end-to-end pipelines, supported by benchmarks incorporating both real hardware metrics and proxy cost models (Li et al., 2021, Ahmad et al., 9 Apr 2024), and leveraging meta-learned and synthetic device generalization strategies (Capuano et al., 1 Apr 2025). This suggests broad applicability for energy-efficient edge intelligence, federated learning under device constraints, and robust embedded medical or IoT analytics.

In summary, hardware-aware NAS-inspired feature selection represents a rigorously defined and rapidly advancing area at the intersection of embedded AI, machine learning, and hardware/software co-design. By embracing search-space expansion, multi-objective formalism, resource-constrained optimization, and surrogate-based evaluation informed by HW-NAS methodologies, it enables high-performing, deployable solutions for modern and future resource-limited computing environments.