Trained Quantization Thresholds (TQT)

• Trained Quantization Thresholds (TQT) are methods that treat quantization clipping values as adaptive, learnable parameters tailored to specific data distributions and network tasks.
• They employ gradient-based optimization techniques, such as straight-through estimators, to balance dynamic range with precision and minimize quantization error.
• TQT enhances model compression and hardware efficiency in deep learning, signal processing, and communications through data-driven quantization strategies.

Trained Quantization Thresholds (TQT) refer to the class of quantization approaches in which the thresholds or clipping values used by quantizers are treated as learnable parameters and adapted through the training process. This methodology is now central to both model compression and efficient inference in neural networks, as well as to certain compressed sensing tasks. The concept extends to deep learning, signal processing, and communications, with implementations spanning both software frameworks and hardware-optimized pipelines. TQT methods are characterized by their ability to reconcile the trade-offs between range and precision, mitigate information loss and magnitude ambiguity, and adaptively fit the quantization function to data- and task-specific requirements.

1. Conceptual Foundations and Rationale

The core motivation behind Trained Quantization Thresholds lies in the limitations of static, heuristic, or fixed quantization boundaries. In traditional quantization, thresholds are often determined by layer statistics (such as min/max or standard deviation) or by direct calibration on a representative dataset. However, these choices may not optimally trade off between dynamic range and quantization precision and may be especially suboptimal for challenging distributions (such as heavy-tailed, highly variable, or multi-modal activations and weights).

TQT addresses this by rendering the quantization thresholds plastic—either as direct optimization variables, codebook entries (e.g., in the case of clustering), or through learnable scaling factors. The thresholds are updated using gradients computed during backpropagation, or iteratively refined via auxiliary estimation schemes (e.g., calibration in compressed sensing (Fang et al., 2013), iterative channel estimation in MIMO (Wang et al., 2017)).

The theoretical underpinning is that by minimizing a task-relevant loss (such as the empirical risk or a statistical distance on outputs), the quantization function can co-adapt to the distribution of data and model parameters, directly controlling the induced quantization error and information loss. This enables near-floating-point accuracy even under stringent constraints on bit-width or hardware efficiency (Jain et al., 2019).

2. Methodologies in Deep Neural Networks

In deep learning contexts, TQT is exemplified by several paradigms:

• K-means–based quantization with centroid retraining: As in "Deep Compression" (Han et al., 2015), network weights are clustered and mapped to centroids, with the centroids adjusted using the sum of gradients from all assigned weights. The effective thresholds—cluster boundaries—are thus tuned to minimize network loss during retraining.
• Quantizer parameters as learnable variables: In uniform symmetric quantization, as formalized in (Jain et al., 2019), the clipping threshold $t$ (which determines the scale $s$) is optimized via gradient descent. The forward quantization is

$$q(x; s) = \operatorname{clip}(\lfloor x/s \rceil, n, p) \cdot s$$

and the backward gradient with respect to $\log_2 t$ encodes the influence of $t$ on the quantization error, balancing range and precision.

• Ternary and non-uniform quantization with learned scaling and thresholds: Methods such as Trained Ternary Quantization (TTQ) (Zhu et al., 2016) and simultaneous optimization with truncated Gaussian approximation (He et al., 2018) introduce trainable scaling factors and threshold parameters directly into the quantizer. For ternary quantization, both the assignments (e.g., which weights map to $\pm W^p$ or zero) and the scale values are optimized jointly:

$$w^{t}_l = \begin{cases} W_l^p & \text{if } \tilde{w}_l>\Delta_l \ 0 & |\tilde{w}_l| \leq \Delta_l \ -W_l^n & \tilde{w}_l < -\Delta_l \end{cases}$$

with $\Delta_l$ as a trainable or heuristic threshold.

• Adaptive step-size and non-uniform quantization: Adaptive quantization modules (e.g., ASQ (Zhou et al., 24 Apr 2025)) utilize small neural modules to generate step sizes conditioned on input activation statistics, extending TQT to handle input-dependence and complex distributions.

Software frameworks such as TensorQuant (Loroch et al., 2017) provide infrastructure to simulate, train, and evaluate layer- and subunit-dependent quantization thresholds, supporting both fixed and learned configurations.

3. Theoretical Properties and Optimization

The optimization of quantization thresholds often involves non-trivial gradient estimation due to the presence of discrete and piecewise functions (i.e., the quantizer). TQT methods rely on variations of the straight-through estimator (STE) to enable gradient flow through non-differentiable operations. In (Jain et al., 2019), for instance, a careful STE is constructed to align the thresholds' gradients with the empirically observed trade-off between dynamic range and quantization error. The gradient with respect to the threshold $t$ is

$$\frac{\partial q(x; s)}{\partial \log_2 t} = s \ln(2) \cdot (\lfloor x/s \rceil - x/s)$$

inside the clipping region, facilitating threshold adaptation.

Nonuniform and quantizer-shape–adaptive strategies (such as N2UQ (Liu et al., 2021)) further extend TQT by learning input thresholds for histogram binning, subject to entropy-preserving regularization, and use generalized STEs for stable and fine-grained threshold training.

These theoretical advances result in models where the quantization function is data-adaptive, minimizes quantization-induced loss, and achieves robustness against model or data distribution shifts.

4. Practical Applications and Performance Impacts

The practical impact of TQT spans multiple domains:

• Deep learning for computer vision and NLP: TQT enables 8-bit and even 4-bit quantized models to recover near–float-precision accuracy, including on architectures classically considered sensitive to quantization (e.g., MobileNet, ResNet variants, segmenters like Q-SAM2 (Farronato et al., 11 Jun 2025), and LLMs with PreQuant (Gong et al., 2023)).
• Signal processing and compressed sensing: Adaptive quantization of one-bit compressed sensing and channel estimation in MIMO systems (Fang et al., 2013, Wang et al., 2017) achieves theoretical performance limited only by the threshold deviation $\|\delta\|_2$, resolving ambiguity and reducing the required training overhead.
• Hardware efficiency: By constructing quantizers with power-of-two scale factors and per-tensor scaling (Jain et al., 2019), or via hardware-friendly mixed-precision blocks (Habi et al., 2020) and post-training schemes (Habi et al., 2021), TQT-based models facilitate direct mapping to fixed-point hardware, enable efficient bit-shifting in runtime, and minimize memory bandwidth.
• Task-agnostic and post-training quantization: Strategies such as outlier-aware fine-tuning (Gong et al., 2023), module-wise error minimization (Bai et al., 2021), and adaptive threshold scaling (Goncharenko et al., 2018) enable rapid, data-efficient quantization.

Performance metrics consistently indicate that well-tuned TQT methodologies reduce the gap to full-precision baselines to within 1% on classification benchmarks, and can deliver substantial resource and speed gains. In some cases, adaptive quantization thresholds even yield accuracy improvements due to the regularization effect and better alignment of quantization regions with task-relevant distributions (Zhou et al., 24 Apr 2025).

5. Representative Algorithms and Implementation Considerations

Implementing TQT involves several design and practical trade-offs:

• Gradient estimation and stability: The choice of STE, training in log-domain (Jain et al., 2019), and optimizer settings are critical for stability and convergence. TQT requires careful handling of learning rates and gradient normalization.
• Quantizer form and hardware constraints: Uniform symmetric quantizers with power-of-two thresholds are preferred for hardware efficiency, enabling bit-shift scaling and simpler arithmetic (Jain et al., 2019, Habi et al., 2021). Non-uniform strategies using lookup tables (e.g., POST quantization (Zhou et al., 24 Apr 2025)) balance hardware constraints and representational adequacy.
• Fine-grained vs. global thresholding: Per-layer, per-channel, and per-filter scaling is possible. Adaptive schemes (e.g., through small neural modules or per-filter trainable scalars (Goncharenko et al., 2018, Zhou et al., 24 Apr 2025)) can better fit varying distributions and dynamic network states.
• Subunit and topology-dependency: The optimal bitwidth and precision requirements are topology-dependent, motivating the need for flexible simulation and profiling tools as provided by TensorQuant (Loroch et al., 2017).
• Integration into training and inference pipelines: TQT methods integrate with both quantization-aware training (QAT) and post-training quantization (PTQ), with retraining sometimes required for centroids or thresholds (Han et al., 2015, He et al., 2018, Bai et al., 2021).

Typical TQT workflow (in uniform quantization) follows the sequence:

1. Initialize thresholds (either via statistics or calibration).
2. Insert quantization function into forward path; thresholds are treated as parameters.
3. Train (or fine-tune) both model weights and thresholds using a task loss, employing STE or its generalizations.
4. For hardware, constrain scale factors and thresholds as necessary.
5. Optionally, employ retraining or outlier-aware mask updates for continual adaptation.

6. Current Developments and Future Directions

Recent research extends TQT in several directions:

• Ultra-low-bit and dynamic quantizers: Advances include robust quantization for as low as 2-bits by coupling calibrated initialization (e.g., via Frobenius norm minimization) with threshold-adaptive QAT (Farronato et al., 11 Jun 2025), and training-robust quantizers ready for truncation or resource adaptation via bit-shifting (Kim et al., 13 Jun 2025).
• Mixed-precision and adaptive precision allocation: Utilizing distributions over precision/threshold pairs with Gumbel-Softmax estimators (Habi et al., 2020) or information-theoretic methods for bit allocation (Diao et al., 2022).
• Nonuniform-to-uniform and entropy-aware quantization: Learning flexible input thresholds while enforcing uniform hardware-friendly levels (Liu et al., 2021) and preserving information via entropy regularization.
• Functional compression and module-level retraining: Approaches like module-wise reconstruction minimization (Bai et al., 2021) segment large networks for scalable and parallelizable quantization.
• Task-agnostic pre-quantization and outlier correction: Selecting and retraining only a small fraction of parameters responsible for post-quantization error (Gong et al., 2023).

Adoption of these newer methods is facilitated by open-source frameworks (such as Graffitist (Jain et al., 2019)), code repositories, and increasingly modular training and calibration APIs.

Ongoing research further seeks to:

• Harmonize TQT with continual/adaptive learning.
• Jointly learn activation, weight, and even gradient quantization thresholds.
• Co-optimize for energy efficiency, redundancy, and robustness.
• Integrate TQT with specialized hardware accelerators and compiler pipelines.

7. Limitations, Challenges, and Comparative Considerations

While TQT provides substantial gains, several challenges persist:

• Stability and hyperparameter sensitivity: Selecting clipping ranges, initialization values, and learning rates is crucial, especially in very low-bit or mixed-precision regimes.
• Hardware-software co-design: Perfectly mapping the learned thresholds to hardware-implementable quantizers sometimes requires additional constraints (e.g., power-of-two alignment) which may limit the flexibility of the learned solution (Habi et al., 2021, Habi et al., 2020).
• Quantization–truncation mismatch: Methods such as TruncQuant (Kim et al., 13 Jun 2025) highlight the necessity of designing training functions that directly match at-runtime behavior.
• Model/architecture dependence: Quantization sensitivity can vary across architectures, layers, and even data domains, necessitating profiling and, in some cases, topology-dependent threshold allocation (Loroch et al., 2017).

Despite these challenges, TQT remains a primary mechanism for closing the accuracy gap between low-bit quantized and full-precision models, supporting their deployment in resource-limited environments and broadening the applicability of deep learning systems across signal processing and communications.