Simplify: A Python library for optimizing pruned neural networks

Bragagnolo, Andrea; Barbano, Carlo Alberto

doi:10.1016/j.softx.2021.100907

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…From this, we observe that reducing the complexity of the backbone would result in the overall reduction of the complexity for the entire model N , and towards this end pruning has already proved to be an effective approach [9,10]. Pruning approaches can be divided into two groups.…”

Section: Effect Of Pruned Backbones To Capsule Layersmentioning

confidence: 91%

“…Bragagnolo et al [20] showed that structured sparsity, despite removing significantly less parameters from the model, yields lower model's memory footprint and inference time. When pruning a network in a structured way, a simplification step which practically reduces the rank of the matrices is possible; on the other side, encoding unstructured sparse matrices lead to representation overheads [10].…”

Section: Effect Of Pruned Backbones To Capsule Layersmentioning

confidence: 99%

“…To this end, pruning methods (which reduce the size and complexity of a neural network model) have gained more and more attention lately [9]. Learning sparse topologies can effectively reduce the network footprint and speedup execution with negligible performance loss [10]. So, why not exploiting these techniques also for CapsNets?…”

Section: Introductionmentioning

confidence: 99%

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Towards Efficient Capsule Networks

Renzulli

Grangetto

2022

2022 IEEE International Conference on Image Processing (ICIP)

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From the moment Neural Networks dominated the scene for image processing, the computational complexity needed to solve the targeted tasks skyrocketed: against such an unsustainable trend, many strategies have been developed, ambitiously targeting performance's preservation. Promoting sparse topologies, for example, allows the deployment of deep neural networks models on embedded, resourceconstrained devices. Recently, Capsule Networks were introduced to enhance explainability of a model, where each capsule is an explicit representation of an object or its parts. These models show promising results on toy datasets, but their low scalability prevents deployment on more complex tasks. In this work, we explore sparsity besides capsule representations to improve their computational efficiency by reducing the number of capsules. We show how pruning with Capsule Network achieves high generalization with less memory requirements, computational effort, and inference and training time.

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Section: Effect Of Pruned Backbones To Capsule Layersmentioning

confidence: 91%

Section: Effect Of Pruned Backbones To Capsule Layersmentioning

confidence: 99%

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Towards Efficient Capsule Networks

Renzulli

Grangetto

2022

2022 IEEE International Conference on Image Processing (ICIP)

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“…It is worth pointing out that, in order to exploit the structured sparsity introduced in the network, the zeroed neurons have to be removed from the architecture. For this operation we used the Simplify library [20]. Quantization Alongside pruning, quantization is one of the most widely adopted compression methods.…”

Section: Methodsmentioning

confidence: 99%

Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization

Flich

Medina

Catalan

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Neural networks (NN) for image processing in embedded systems expose two conflicting requirements: increasing computing power needs as models become more complex and constrained resource budget. In order to alleviate this problems, model compression based on quantization and pruning techniques are common. Derived models then need to fit on reconfigurable systems such as FPGAs for the embedded system to work properly. In this paper, we present HLSinf, an open source framework for the development of custom NN accelerators for FPGAs which provides efficient support to quantized and pruned NN models. With HLSinf, significant inference speedups can be obtained for typical medical image-based applications. In particular, we obtain up to 90x speedup factor when we combine quantization/pruning with the flexibility of HLSinf compared to CPU.

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“…We use the PyTorch method[17] to set to 0 the weights with smaller L 2 norm, and the Simplify library[18] to remove them from the DNN. This article has been accepted for inclusion in a future issue of this journal.…”

mentioning

confidence: 99%

Tuning DNN Model Compression to Resource and Data Availability in Cooperative Training

Malandrino,

Giacomo,

Karamzade

et al. 2024

IEEE/ACM Trans. Networking

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Model compression is a fundamental tool to execute machine learning (ML) tasks on the diverse set of devices populating current-and next-generation networks, thereby exploiting their resources and data. At the same time, how much and when to compress ML models are very complex decisions, as they have to jointly account for such aspects as the model being used, the resources (e.g., computational) and local datasets available at each node, as well as network latencies. In this work, we address the multi-dimensional problem of adapting the model compression, data selection, and node allocation decisions to each other: our objective is to perform the DNN training at the minimum energy cost, subject to learning quality and time constraints. To this end, we propose an algorithmic framework called PACT, combining a time-expanded graph representation of the training process, a dynamic programming solution strategy, and a data-driven approach to the estimation of the loss evolution. We prove that PACT's complexity is polynomial, and its decisions can get arbitrarily close to the optimum. Through our numerical evaluation, we further show how PACT can consistently outperform state-of-the-art alternatives and closely matches the optimal energy consumption.

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Simplify: A Python library for optimizing pruned neural networks

Cited by 11 publications

References 4 publications

Towards Efficient Capsule Networks

Towards Efficient Capsule Networks

Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization

Tuning DNN Model Compression to Resource and Data Availability in Cooperative Training

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