Squeezing Neural Networks: A New Approach to Memory-Efficient Inference

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Author: Denis Avetisyan

Researchers have developed a compression technique that dramatically reduces the memory footprint of complex neural networks, paving the way for deployment on edge devices.

SHARe-KAN, employing Int8 quantization, demonstrates resilience against decay by achieving accuracy competitive with its denser counterpart-despite a seventeenfold reduction in model size-and nearing the performance of ResNet-50 MLP within a compact 12.91 MB footprint, suggesting efficient information retention even under significant compression.
SHARe-KAN, employing Int8 quantization, demonstrates resilience against decay by achieving accuracy competitive with its denser counterpart-despite a seventeenfold reduction in model size-and nearing the performance of ResNet-50 MLP within a compact 12.91 MB footprint, suggesting efficient information retention even under significant compression.

SHARe-KAN leverages holographic vector quantization and cache optimization to achieve 88x memory reduction in Kolmogorov-Arnold Networks.

Despite recent advances in neural network efficiency, Kolmogorov-Arnold Networks (KANs) remain hampered by substantial memory requirements stemming from their dense parameterization. This work introduces SHARe-KAN: Holographic Vector Quantization for Memory-Bound Inference, a novel compression framework that exploits the inherent holographic topology of Vision KANs via functional redundancy and vector quantization. We demonstrate an 88$\times$ reduction in runtime memory alongside maintained accuracy on PASCAL VOC, achieved through a combination of SHARe-KAN and a hardware-aware compiler. Can this approach unlock KAN deployment in truly resource-constrained environments and pave the way for more efficient spline-based architectures?

The Allure of Functional Spaces: Beyond Scalar Constraints

Conventional deep learning architectures fundamentally depend on scalar weights to modulate signals between layers. While remarkably successful, this approach presents inherent limitations in both expressive power and computational efficiency. Each weight represents a single numerical value, restricting the complexity of transformations a network can learn and necessitating vast numbers of parameters, especially for intricate tasks. This reliance on numerous scalars contributes to substantial memory requirements and hinders generalization capabilities, as the network struggles to represent complex relationships with limited flexibility. Consequently, the performance of these networks often plateaus, demanding ever-larger datasets and computational resources to achieve incremental gains, highlighting the need for alternative approaches that can achieve comparable or superior results with fewer parameters and increased representational capacity.

Kolmogorov-Arnold Networks (KANs) represent a departure from conventional deep learning architectures by embracing functional transformations instead of relying solely on scalar weights. These networks utilize B-spline basis functions – smooth, piecewise polynomial functions – to map inputs to outputs, offering a significantly more expressive capacity than traditional methods. This approach allows KANs to represent complex functions with fewer parameters, potentially leading to more efficient models. Rather than learning individual weights, the network learns the coefficients of these B-spline functions, effectively learning a function itself. The resulting functional representation allows for inherent generalization and robustness, as small changes in input don’t necessarily lead to drastic changes in output, and enables the network to capture intricate relationships within data that might be missed by scalar-weighted systems. This foundational shift towards learning functions, rather than just weights, holds promise for advancements in both model compression and enhanced reasoning capabilities.

The transition to functional networks, such as Kolmogorov-Arnold Networks, suggests a pathway towards significantly enhanced model compression and reasoning. By representing weights as continuous functions – specifically through B-spline basis functions – these networks can achieve the same representational power as traditional networks with far fewer parameters. This efficiency stems from the ability to generalize across inputs; a single function can map a range of values, reducing the need for individual weights for each connection. Furthermore, this functional representation facilitates a more natural encoding of symmetries and invariances present in data, potentially leading to improved generalization and reasoning capabilities. The network essentially learns how to transform data, rather than memorizing specific weight values, offering a more robust and adaptable approach to complex tasks and hinting at a future where models can learn more like humans.

Vision KANs exhibit a catastrophic performance drop with magnitude-based pruning, unlike standard MLPs which degrade gradually, suggesting information is distributed rather than concentrated within the network.
Vision KANs exhibit a catastrophic performance drop with magnitude-based pruning, unlike standard MLPs which degrade gradually, suggesting information is distributed rather than concentrated within the network.

Compressing the Functional Landscape: SHARe-KAN’s Approach

Kernel Attention Networks (KANs) demonstrate strong performance in various machine learning tasks, but their computational demands present a significant barrier to practical implementation. The complexity arises from the quadratic scaling of attention mechanisms with input sequence length, requiring substantial memory and processing power. This computational expense limits deployment on resource-constrained devices and increases inference latency, hindering real-time applications. Therefore, effective compression techniques are essential to reduce the model size and computational cost of KANs without substantial performance degradation, enabling broader accessibility and usability.

SHARe-KAN reduces model size through gain-shape-bias decomposition, which separates the KAN’s weight matrices into gain, shape, and bias components. Each component is then individually compressed using vector quantization, a process that represents data points as vectors chosen from a finite set of codebook vectors. This quantization reduces the number of bits required to store each weight, resulting in a smaller overall model size. The gain component, representing scaling factors, is particularly amenable to quantization due to its limited range, further contributing to compression efficiency.

SHARe-KAN achieves substantial model compression through the independent processing of gain, shape, and bias components within Kernel Attention Networks (KANs). This decomposition enables the application of vector quantization techniques to each component, resulting in a reduced model size while preserving performance. Specifically, SHARe-KAN attains a mean Average Precision (mAP) of 84.74%, representing a minimal accuracy reduction of 0.49% compared to the 85.23% mAP achieved by the original, dense KAN implementation. This demonstrates a favorable trade-off between compression and maintained accuracy for practical deployment scenarios.

LUTHAM: A Runtime Designed for Functional Efficiency

Conventional deep learning runtimes are optimized for dense matrix operations and assume a data-centric representation where parameters are stored as numerical values. Kernel-based ANN (KAN) models, however, utilize a functional representation based on quantized splines, requiring evaluation of functions rather than simple matrix multiplications. This mismatch leads to significant inefficiencies as existing runtimes incur substantial overhead from unnecessary data movement, function call dispatch, and lack of specialized optimizations for spline evaluation. Furthermore, the sparse and irregular nature of KANs’ functional representation is not effectively handled by runtimes designed for dense, regularly structured models, hindering performance and scalability.

LUTHAM is a runtime environment specifically engineered for the efficient execution of quantized spline-based models. It employs static memory planning to pre-allocate and manage memory resources during model loading, thereby eliminating runtime allocation overhead. Furthermore, LUTHAM utilizes zero-copy execution, a technique that minimizes data transfer between memory locations by directly processing data in its original location. This combination of static memory planning and zero-copy execution substantially reduces computational overhead and maximizes throughput during inference, which is particularly beneficial for models utilizing a functional representation like KANs.

The integration of static memory planning and zero-copy execution within the LUTHAM runtime demonstrably reduces operational overhead and increases processing speed for compressed KANs. This optimization strategy yields a significant decrease in runtime memory footprint, achieving an 88x reduction from 1.13 GB to 12.91 MB. By pre-allocating and directly accessing memory, LUTHAM avoids redundant data copying and allocation delays, thereby maximizing throughput and enabling the deployment of large KAN models within resource-constrained environments.

LUTHAM’s design enables the deployment of compressed KAN models directly within the 40MB L2 cache of an NVIDIA A100 GPU, eliminating the need for frequent data transfers between GPU memory and system RAM. This cache residency is achieved without significant performance degradation, demonstrated by less than 1% mean Average Precision (mAP) loss when evaluated on the PASCAL VOC detection dataset. This minimal accuracy impact confirms that LUTHAM’s optimizations effectively maintain model performance while drastically reducing memory bandwidth requirements and latency.

The Persistence of Information: Redundancy and Low-Rank Structure

Kernelized Attention Networks (KANs) possess a remarkable ability to maintain performance even with substantial reductions in parameters, a consequence of inherent functional redundancy within their architecture. This means the network encodes information in a highly efficient manner, where multiple parameters contribute to the same underlying function; thus, removing some parameters doesn’t immediately lead to a loss of information. The network isn’t simply memorizing data, but rather learning a compressed representation, allowing it to generalize effectively even when subjected to aggressive compression techniques. This characteristic differentiates KANs from more conventional neural networks, opening pathways for deployment in resource-constrained environments without significant performance degradation and hinting at a more robust and efficient internal representation of data.

Kernel Attention Networks (KANs) demonstrate a remarkable ability to maintain performance even with substantial compression, a characteristic stemming from the low-dimensional structure of the functions they represent. Instead of requiring a vast number of parameters to capture complex relationships, KANs effectively operate within a lower-rank functional space – analogous to representing a high-resolution image with a smaller set of essential basis components. This inherent property means the network’s core functionality is not spread uniformly across all parameters, but rather concentrated within a more compact subspace. Consequently, KANs can achieve significant compression rates without proportionally degrading performance, as much of the apparent redundancy is due to overparameterization within this lower-dimensional representation. The network essentially encodes information efficiently, similar to how principal component analysis reduces dimensionality in data, enabling a powerful and streamlined operation.

Kernel Attention Networks (KANs) present a unique challenge to conventional model compression techniques; localized pruning, a common strategy for reducing network size, proves remarkably ineffective. Studies reveal that removing just 10% of connections via localized pruning causes a dramatic 40-point drop in mean Average Precision (mAP), plummeting from 85.23% to 45%. This stark performance degradation isn’t due to the loss of critical parameters in isolation, but rather the network’s “holographic” topology-where information is distributed and encoded across the entire network. Consequently, selective removal of connections disrupts this global representation, severely impacting performance, and underscoring the necessity of employing compression strategies that address the network as a unified whole, rather than through localized interventions.

Rigorous testing of this compression strategy on benchmark datasets – including the challenging COCO (Common Objects in Context) and PASCAL VOC – confirms its practical efficacy. Results consistently demonstrate that knowledge-augmented networks (KANs) maintain high performance even with substantial reductions in parameters, a feat not readily achievable through conventional pruning methods. Specifically, these evaluations showcase the ability to compress KANs without incurring the significant accuracy drops observed in other architectures, validating the theoretical underpinnings of leveraging functional redundancy and low-rank structure for efficient model design. This success across diverse image recognition tasks underscores the potential for broader application of this approach to other complex machine learning problems.

The pursuit of efficient neural networks, as demonstrated by SHARe-KAN’s 88x memory reduction, echoes a fundamental principle of resilient systems. It isn’t merely about minimizing resource consumption, but about gracefully adapting to constraints. As Alan Turing observed, “Sometimes people who are unhappy tend to look at the world as hostile.” Similarly, a system encountering resource limitations doesn’t simply fail; it reveals opportunities for functional redundancy and innovative compression – like the holographic topology employed in SHARe-KAN. The system’s response, its adaptation through vector quantization and cache optimization, becomes a step towards maturity, transforming limitations into a testament to its enduring architecture. This is not about avoiding the ‘hostility’ of constraints, but about building systems that thrive within them.

The Long View

The demonstrated compression-a reduction in memory footprint by a factor of 88-is not merely an engineering feat, but a temporary reprieve. Every architecture, however efficient, accrues the weight of its limitations. The success of SHARe-KAN hinges on a carefully constructed redundancy, a holographic topology that, while elegant, will inevitably succumb to the pressures of scaling. The true challenge lies not in achieving compression, but in understanding the precise nature of that which is lost in the process-what functional ghosts remain, and how their absence impacts the network’s capacity for generalization.

Future work must address the inherent fragility of vector quantization in the face of evolving data distributions. A static codebook, however cleverly constructed, is a brittle foundation. Adaptive quantization schemes, informed by the network’s own internal state, offer a potential, though complex, path forward. More fundamentally, research should explore whether the very notion of a discrete codebook is a necessary constraint, or if continuous, differentiable approximations could yield a more robust and graceful decay.

The pursuit of efficiency is, in a sense, a deferral of entropy. SHARe-KAN buys time, allowing for deployment on resource-constrained devices. But time, ultimately, is not the goal; it is the medium in which systems reveal their inherent limitations. The enduring questions remain: what constitutes meaningful representation, and how can we build architectures that age, not with catastrophic failure, but with a measured and predictable decline?

Original article: https://arxiv.org/pdf/2512.15742.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-19 22:46