Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach

• URL: http://arxiv.org/abs/2604.09988v1
• Date: Sat, 11 Apr 2026 02:16:33 GMT
• Title: Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach
• Abstract summary: We present a concept-based pruning technique for Deep Neural Networks (DNNs)<n>It guides pruning decisions using human-interpretable concepts, such as features, colors, and classes.<n>Our results show that concept-based pruning efficiently generates much smaller, effective pruned DNNs.
• Score: 5.380073435052757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models are often massive, with millions or billions of parameters, and require substantial computational power, RAM, and storage. This becomes a limitation in practical scenarios where strict size and resource constraints must be respected. In this paper, we present a novel concept-based pruning technique for DNNs that guides pruning decisions using human-interpretable concepts, such as features, colors, and classes. This is particularly important in a software engineering context, as DNNs are integrated into systems and must be pruned according to specific system requirements. Our concept-based pruning solution analyzes neuron activations to identify important neurons from a system requirements viewpoint and uses this information to guide the DNN pruning. We assess our solution using the VGG-19 network and a dataset of 26'384 RGB images, focusing on its ability to produce small, effective pruned DNNs and on the computational complexity and performance of these pruned DNNs. We also analyzed the pruning efficiency of our solution and compared alternative configurations. Our results show that concept-based pruning efficiently generates much smaller, effective pruned DNNs. Pruning greatly improves the computational efficiency and performance of DNNs, properties that are particularly useful for practical applications with stringent memory and computational time constraints. Finally, alternative configuration options enable engineers to identify trade-offs adapted to different practical situations.

Related papers

• S$^2$NN: Sub-bit Spiking Neural Networks [53.08060832135342]
  Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence.<n>Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks.<n>We propose Sub-bit Spiking Neural Networks (S$2$NNs) that represent weights with less than one bit.
  arXiv  Detail & Related papers  (2025-09-29T04:17:44Z)
• QP-SNN: Quantized and Pruned Spiking Neural Networks [10.74122828236122]
  Spiking Neural Networks (SNNs) leverage spikes to encode information and operate in an event-driven manner.<n>We propose a hardware-friendly and lightweight SNN, aimed at effectively deploying high-performance SNN in resource-limited scenarios.
  arXiv  Detail & Related papers  (2025-02-09T13:50:59Z)
• Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization [1.0235078178220354]
  We propose an automated framework to compress Deep Neural Networks (DNNs) in a hardware-aware manner by jointly employing pruning and quantization. Our framework achieves $39%$ average energy reduction for datasets $1.7%$ average accuracy loss and outperforms significantly the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-12-23T18:50:13Z)
• An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks [13.271286153792058]
  Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. This paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties.
  arXiv  Detail & Related papers  (2023-07-29T06:27:28Z)
• Neural network relief: a pruning algorithm based on neural activity [47.57448823030151]
  We propose a simple importance-score metric that deactivates unimportant connections. We achieve comparable performance for LeNet architectures on MNIST. The algorithm is not designed to minimize FLOPs when considering current hardware and software implementations.
  arXiv  Detail & Related papers  (2021-09-22T15:33:49Z)
• Pruning and Slicing Neural Networks using Formal Verification [0.2538209532048866]
  Deep neural networks (DNNs) play an increasingly important role in various computer systems. In order to create these networks, engineers typically specify a desired topology, and then use an automated training algorithm to select the network's weights. Here, we propose to address this challenge by harnessing recent advances in DNN verification.
  arXiv  Detail & Related papers  (2021-05-28T07:53:50Z)
• Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks [52.32646357164739]
  We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF) The proposed SIDNN is compatible with a broad range of OPF schemes. It can be seamlessly integrated in other learning-to-OPF schemes.
  arXiv  Detail & Related papers  (2021-03-27T00:45:23Z)
• dNNsolve: an efficient NN-based PDE solver [62.997667081978825]
  We introduce dNNsolve, that makes use of dual Neural Networks to solve ODEs/PDEs. We show that dNNsolve is capable of solving a broad range of ODEs/PDEs in 1, 2 and 3 spacetime dimensions.
  arXiv  Detail & Related papers  (2021-03-15T19:14:41Z)
• Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey [77.99182201815763]
  Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings. DNNs are often treated as black box systems, which complicates their evaluation and validation. One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations.
  arXiv  Detail & Related papers  (2020-06-30T14:56:05Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)
• PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning [57.20262984116752]
  We introduce a new dimension, fine-grained pruning patterns inside the coarse-grained structures, revealing a previously unknown point in design space. With the higher accuracy enabled by fine-grained pruning patterns, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency.
  arXiv  Detail & Related papers  (2020-01-01T04:52:07Z)
• SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Network [17.928105470385614]
  We propose an intelligent tiled-based mechanism for increasing the adaptiveness of RNN, in order to efficiently handle the data dependencies. Sharp achieves 2x, 2.8x, and 82x speedups on average, considering different RNN models and resource budgets.
  arXiv  Detail & Related papers  (2019-11-04T14:51:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.