Quantization-Based Local Deployment

• Quantization-based local deployment is a set of techniques that reduce numerical precision in DNNs to enable efficient on-device inference on resource-constrained platforms.
• It integrates localized quantization, adaptive mixed-precision, and integer nesting to balance accuracy, memory, and compute trade-offs.
• The approach supports resource-aware optimizations such as dynamic bit-width assignment and hardware-friendly integer arithmetic to meet strict energy and memory limitations.

Quantization-based local deployment refers to the suite of methodologies, frameworks, and algorithmic techniques that enable deployment of deep neural networks (DNNs) and other machine learning models on resource-constrained edge devices by reducing numerical precision (bit-width) for weights, activations, and sometimes even model structure. This approach is motivated by the need to reconcile the high computational complexity and memory requirements of modern DNNs with the limited compute, memory, and energy profiles characteristic of embedded platforms such as microcontrollers, IoT nodes, mobile devices, and FPGAs. Quantization-based local deployment encompasses innovations in quantization algorithms, resource-aware strategies, and system-level engineering that allow advanced learning models to execute efficiently and accurately in situ, minimizing the need for remote/cloud inference.

1. Localized and Adaptive Quantization Schemes

Traditional dynamic fixed-point quantization applies a single global scale/step per layer, often resulting in suboptimal accuracy due to global quantization error amplification. Localized quantization schemes partition network parameters—such as a weight tensor—into multiple local regions, each of which is assigned its own quantization step. Mathematically, for region $k$,

$S_k = \frac{x_{k, \max} - x_{k, \min}}{2^n - 1}$

and the quantized value is given by

$$Q_k(x) = \text{round}\left(\frac{x - x_{k, \min}}{S_k}\right)$$

(Yang et al., 2018). This leads to finer control of quantization error, particularly in regions exhibiting lower dynamic range, and allows tuning the granularity from kernel-size to even $3 \times 3$ patch regions, resulting in accuracy improvements (e.g., boosting VGG-16 top-1 from 50.2% to 68.3% at 2-bit).

Other adaptive approaches, such as Dynamic Network Quantization (DNQ), employ a reinforcement learning–based controller to select optimal layer-wise bit-width assignments, optimizing a reward that balances accuracy and compression ratio:

$$R = \text{Accuracy} + \lambda \cdot \text{Compression Ratio}$$

and use quantization distance as a local importance criterion for progressive quantization and retraining (Xu et al., 2018).

2. Frameworks and Inference Engineering for Edge and IoT Devices

Quantization-based local deployment is distinguished by specialized frameworks that convert neural networks from standard floating-point representations to integer-only execution formats. The NEMO framework exemplifies this by providing four representations: FullPrecision, FakeQuantized, QuantizedDeployable, and IntegerDeployable. The IntegerDeployable mode ensures all model computations, including batch normalization (BN) and pooling, are folded into integer arithmetic:

$t_i = \alpha_t + \epsilon_t \cdot q_i$

where all operations—including requantization and pooling—use integer arithmetic and only a unique $\epsilon$ value (quantum) propagates across tensor boundaries (Conti, 2020). This is critical for efficient deployment on platforms lacking dedicated floating-point units.

Uniform quantization is often employed for embedded microcontrollers since it facilitates low-overhead implementation (shift operations) when power-of-two scaling is used. The MicroAI framework automates quantization and code generation for C-based deployment on ARM Cortex-M microcontrollers, demonstrating that 16-bit fixed-point quantization achieves accuracy nearly identical to full-precision baselines across diverse datasets while drastically reducing memory and energy consumption (Novac et al., 2021).

3. Granular Performance-Resource Trade-offs and Mixed Precision

Resource-aware mixed-precision quantization explicitly addresses platform constraints during deployment. Rather than assigning a fixed bit-width network-wide, individual layers (or even subcomponents like MHA, FFN, or normalization blocks) are assigned distinct bit-widths. Fine-grained resource estimation (across LUTs, BRAM, DRAM, DSPs) is used to select quantization configurations that best fit resource thresholds on embedded FPGAs, achieving precision discrepancy within 3% of actual deployment metrics (Ling et al., 4 Oct 2024).

Compiler-based frameworks, such as QuantuneV2, exploit local metrics—layer-wise SNR, MSE, SQNR delta—and operator fusion at compile time to assign mixed precision only where required, avoiding retraining and incurring minimal computational overhead, with O($n$) overall complexity. This compiler-based design achieves up to 10.28% accuracy gains and 12.52% faster execution across standard benchmarks (Kim et al., 13 Jan 2025).

Elastic and “layer-specific adaptive” quantization has been further extended in LLM contexts, where a family of models at multiple bit-width configurations (the “Elastic Quantization Models” or EQMs) are automatically constructed using tree-search strategies and block importance ranking, enabling on-the-fly switching and fine-grained memory adaptation (e.g., in 100 MB increments) with 10× storage reduction relative to ensemble approaches (Chai et al., 13 Jan 2025, Zeng et al., 24 Dec 2024).

4. Deployment-Aware Post-Training and Integer Nesting Approaches

Standard post-training quantization (PTQ) is limited by its creation of a single, fixed bit-width artifact. Recent research has introduced “integer nesting” quantization (NestQuant), which decomposes quantized weights into a higher-bit component and a low-bit remainder:

$$w_{\text{int}} = \text{LeftShift}(w_{\text{high}}, l) + w_\text{low}$$

with only one nested model transmitted/stored. This allows dynamic switching between full-bit and part-bit deployment regimes with “paging” of the additional lower bits as needed, reducing storage and switching overheads by over 75% compared to storing separate models, while retaining near-identical accuracy (e.g., INT8-nested-INT6 achieving 78.1%/77.9% top-1 on ResNet-101 full-bit/part-bit respectively) (Xie et al., 22 Jun 2025).

5. System and Communication Efficiency in Distributed Settings

In distributed optimization and federated learning, gradient and model quantization is essential to address communication bottlenecks. The Qsparse-local-SGD algorithm fuses gradient sparsification and quantization with local computations and an error compensation mechanism:

$$x_{t+1} = x_t - \frac{\eta_t}{R} \sum_{r=1}^R Q(\text{Comp}_k(m_t^{(r)} + (x_t - \hat{x}_{t+1/2}^{(r)})))$$

This approach achieves communication compression ratios of up to 1000× relative to full-precision SGD, with unchanged first-order convergence properties (Basu et al., 2019).

Edge deployment of LLMs using mixture-of-experts architectures introduces new system-level challenges. Hessian-aware quantization (HAQ) jointly quantizes activations and weights via activation smoothing (minimizing

$s = \arg\min \|Q(W \cdot s) Q(s^{-1} X) - W X\|$

) and expert-level CPU-GPU collaborative inference with runtime cost prediction and LRU caching, reducing GPU memory by 60% and maintaining performance within 0.02 PPL of FP16 baselines (Zhang et al., 10 Aug 2025).

6. Specialized Quantization for Generative and Sensing Models

Generative models, such as latent diffusion models (LDMs), and real-time wireless sensor networks pose specialized requirements. For LDMs, hybrid quantization strategies use time-averaged signal-to-quantization-noise ratio (SQNR) to identify sensitive blocks or modules and allocate higher precision accordingly. Local strategies (e.g., SmoothQuant-like channel scaling) are employed where shortcut or transformer projection layers degrade with aggressive quantization, improving FID/SQNR/bitops and maintaining output quality (Yang et al., 2023, Zhang et al., 20 Jul 2025).

In distributed sensing, multilevel entropy-maximizing quantization (MAE) selects thresholds to maximize the average output entropy under both null and alternative hypotheses. This optimizes detection performance for distributed detection while balancing power/bandwidth usage in local sensor deployment (Wahdan et al., 2019).

7. Hardware and Deployment Compatibility Considerations

Post-training quantization frameworks for deployment enforce strict hardware-friendly constraints: uniform, symmetric quantizers with power-of-two thresholds for direct mapping to shift-based integer operations, activation folding of batch normalization, and per-channel scaling for accuracy-retention. The HPTQ framework demonstrates that such design reduces power and latency while providing accuracy within 1% of floating-point baselines across tasks including classification, object detection, and segmentation (Habi et al., 2021).

Implementations that leverage look-up table (LUT) arithmetic further replace multiply-accumulate operations in ultra-low bit networks (<8 bit) for microcontrollers and FPGA accelerators, maximizing the advantage of low-precision representation (Yang et al., 2018).

Summary Table: Key Algorithmic Dimensions

Approach/Framework	Quantization Granularity	Resource Adaptation Mechanism
Local region quantization	Kernel or patch–level regions	Fine for weight/activation distribution per region
Adaptive/mixed precision	Layer/channel-wise, block-wise	Policy or sensitivity-driven (bitwidth per layer)
Integer nesting (NestQuant)	Bitwise weight decomposition	Full/part bit models selectable at runtime
Framework-integrated (NEMO, MicroAI, HPTQ)	Layer-wise, operator-wise	Requantization, BN folding, hardware-specific scaling
Elastic Q (FlexQuant, LSAQ)	Module-wise, ensemble-based	Dynamic, per-device or per-memory-state adaptation

Quantization-based local deployment frameworks now span from rigorous low-level hardware-aware bit manipulation and code synthesis to sophisticated adaptive precision controllers and integer nesting mechanisms. Their goals are to preserve network accuracy as much as possible while delivering substantial computational, memory, and energy savings on constrained edge, embedded, or federated environments. The collective advances in localized quantization, adaptive bit-width assignment, storage/compute elasticity, and hardware compatibility have established quantization-based local deployment as a critical enabler for on-device intelligence at scale (Yang et al., 2018, Xu et al., 2018, Basu et al., 2019, Wahdan et al., 2019, Conti, 2020, Gluska et al., 2020, Novac et al., 2021, Habi et al., 2021, Chen et al., 2022, Yang et al., 2023, Hoque et al., 15 Jan 2024, Ling et al., 4 Oct 2024, Zeng et al., 24 Dec 2024, Chai et al., 13 Jan 2025, Kim et al., 13 Jan 2025, Li et al., 1 Jun 2025, Xie et al., 22 Jun 2025, Zhang et al., 20 Jul 2025, Zhang et al., 10 Aug 2025).