Optimising Neural Network Training Efficiency Through Spectral

Leo Migdal
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optimising neural network training efficiency through spectral

[1]\orgdivDepartment of Computer Engineering, \orgnameFerdowsi University of Mashhad, \orgaddress\streetAzadi Square, \cityMashhad, \postcode9177948974, \stateKhorasan-Razavi, \countryIran Effective data curation is essential for optimizing neural network training. In this paper, we present the Guided Spectrally Tuned Data Selection (GSTDS) algorithm, which dynamically adjusts the subset of data points used for training using an off-the-shelf pre-trained reference model. Based on a pre-scheduled filtering ratio, GSTDS effectively reduces the number of data points processed per batch. The proposed method ensures an efficient selection of the most informative data points for training while avoiding redundant or less beneficial computations. Preserving data points in each batch is performed based on spectral analysis.

A Fiedler vector-based scoring mechanism removes the filtered portion of the batch, lightening the resource requirements of the learning. The proposed data selection approach not only streamlines the training process but also promotes improved generalization and accuracy. Extensive experiments on standard image classification benchmarks, including CIFAR-10, Oxford-IIIT Pet, and Oxford-Flowers, demonstrate that GSTDS outperforms standard training scenarios and JEST, a recent state-of-the-art data curation method, on several key factors. It is shown that GSTDS achieves notable reductions in computational requirements, up to four times; without compromising performance. GSTDS exhibits a considerable growth in terms of accuracy under the limited computational resource usage in contrast to other methodologies. These promising results underscore the potential of spectral-based data selection as a scalable solution for resource-efficient deep learning and motivate further exploration into adaptive data curation strategies.

You can find the code at https://github.com/rezasharifi82/GSTDS. The quality of training data is a cornerstone of deep learning model performance. While large-scale datasets have propelled advancements in natural language processing ([2]), computer vision ([34]), and multimodal learning ([25, 14]), not all data points contribute equally to model training. Datasets often contain redundant, irrelevant, or non-informative samples, which increase computational overhead without significantly enhancing learning outcomes. Identifying and selecting the most informative data points is therefore essential to improve the efficiency of the model, reduce resource consumption, and improve generalization capabilities ([16, 29]). Recent advancements in automated data curation, such as active learning, have demonstrated the potential of dynamically selecting valuable data points based on heuristic approaches, which can result in noticeable training efficiency improvement ([28, 4]).

Spectral analysis, particularly through the use of the Fiedler vector, has emerged as a robust mechanism for scoring data points within batches. This approach captures the geometric relationships between samples, enabling a principled evaluation of their informativeness ([35, 18]). Furthermore, recent studies have shown that processing data collectively at the batch level, rather than individually, significantly improves the assessment of relative informativeness, outperforming traditional methods ([9]). A key challenge in data curation lies in balancing the rigor of sample selection with the model’s accuracy. By integrating spectral scoring with a pre-scheduled filtering ratio, we can achieve precise control over computational costs while maintaining model performance. To ensure resource allocation matches the training process dynamics, we smoothly increase filtering ratio to effectively work with larger batch sizes and enhance final stages of learning ([31]).

Nature Communications volume 12, Article number: 1330 (2021) Cite this article Deep neural networks are usually trained in the space of the nodes, by adjusting the weights of existing links via suitable optimization protocols. We here propose a radically new approach which anchors the learning process to reciprocal space. Specifically, the training acts on the spectral domain and seeks to modify the eigenvalues and eigenvectors of transfer operators in direct space. The proposed method is ductile and can be tailored to return either linear or non-linear classifiers. Adjusting the eigenvalues, when freezing the eigenvectors entries, yields performances that are superior to those attained with standard methods restricted to operate with an identical number of free parameters.

To recover a feed-forward architecture in direct space, we have postulated a nested indentation of the eigenvectors. Different non-orthogonal basis could be employed to export the spectral learning to other frameworks, as e.g. reservoir computing. Machine learning (ML)1,2,3,4 refers to a broad field of study, with multifaceted applications of cross-disciplinary breadth. ML is a subset of artificial intelligence (AI) which ultimately aims at developing computer algorithms that improve automatically through experience. The core idea is that systems can learn from data, so as to identify distinctive patterns and make consequent decisions, with minimal human intervention.

The range of applications of ML methodologies is extremely vast5,6,7,8, and still growing at a steady pace due to the pressing need to cope with the efficiently handling of big data9. Biomimetic approaches to sub-symbolic AI10 inspired the design of powerful algorithms. These latter sought to reproduce the unconscious process underlying fast perception, the neurological paths for rapid decision making, as e.g. employed for faces11 or spoken words12 recognition. An early example of a sub-symbolic brain-inspired AI was the perceptron13, the influential ancestor of deep neural networks (NN)14,15. The perceptron is indeed an algorithm for supervised learning of binary classifiers.

It is a linear classifier, meaning that its forecasts are based on a linear prediction function which combines a set of weights with the feature vector. Analogous to neurons, the perceptron adds up its input: if the resulting sum is above a given threshold the perceptron fires (returns the output value one) otherwise it does not (and the output equals... Modern multilayer perceptrons account for multiple hidden layers with non-linear activation functions. The learning is achieved via minimising the classification error. Single or multilayered perceptrons should be trained by examples14,16,17. Supervised learning requires indeed a large set of positive and negative examples, the training set, labelled with their reference category.

The perceptrons’ acquired ability to perform classification is eventually stored in a finite collection of numbers, the weights and thresholds that were learned during the successive epochs of the supervised training. To date, it is not clear how such a huge collection of numbers (hundred-millions of weights in state of the art ML applications) are synergistically interlaced for the deep networks to execute the assigned... This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Conventional convolutional neural networks (CNNs) present a high computational workload and memory access cost (CMC). Spectral domain CNNs (SpCNNs) offer a computationally efficient approach to compute CNN training and inference.

This paper investigates CMC of SpCNNs and its contributing components analytically and then proposes a methodology to optimize CMC, under three strategies, to enhance inference performance. In this methodology, output feature map (OFM) size, OFM depth or both are progressively reduced under an accuracy constraint to compute performance-optimized CNN inference. Before conducting training or testing, it can provide designers guidelines and preliminary insights regarding techniques for optimum performance, least degradation in accuracy and a balanced performance–accuracy trade-off. This methodology was evaluated on MNIST and Fashion MNIST datasets using LeNet-5 and AlexNet architectures. When compared to state-of-the-art SpCNN models, LeNet-5 achieves up to 4.2× (batch inference) and 4.1× (single-image inference) higher throughputs and 10.5× (batch inference) and 4.2× (single-image inference) greater energy efficiency at a maximum loss... When compared to the baseline model used in this study, AlexNet delivers 11.6× (batch inference) and 5× (single-image inference) increased throughput and 25× (batch inference) and 8.8× (single-image inference) more energy-efficient inference with just...

Keywords: Convolutional neural network, Spectral domain CNN, Energy efficiency, Computational workload, Memory access cost Deep neural networks (DNNs) have recently evolved as the prevalent solution for a range of challenging problems in computer vision, such as image recognition [1], image segmentation [2–4], set based image classification [5], and... Convolutional neural networks (CNNs) [9, 10], a class of DNNs, have achieved unprecedented success in various fields of computer vision, audio analysis, and text processing including–inter alia–object classification [11, 12], object detection [13, 14],... In the last few years, CNNs have been deployed in diverse applications such as autonomous driving [23], navigation systems [24] and flight safety [25] for drones, skin cancer detection [26], COVID-19 prognosis [27], and... In CNNs, convolution layers play a central role in feature-extraction [9–11] but demand high computational resources [29]. They account for 90% of CNN operations [30].

Moreover, deeper CNNs (with more convolution layers), which tend to produce higher accuracy, possess a larger number of parameters. This results in increased memory requirements [29]. However, run-time memory during inference is dominated by storage of intermediate output feature maps [31], even with a batch size of 1 [32]. Memory consumed by these output feature maps (OFMs) can exceed parameter memory by 10 to a 100 times [33]. Storing intermediate OFMs requires off-chip dynamic random access memory (DRAM) accesses, which consume more power and energy than computations [29, 34]. In fact, for devices limited by memory bandwidth, the memory access cost can be the main bottleneck for power consumption and inference latency including in GPU-based platforms [15, 35, 36].

Therefore, the computational, memory and power budgets of even classical CNNs (with only a few convolutional layers) are such that deploying them in embedded systems or mobile platforms is extremely challenging [29, 30, 32,... ����T��F����$b��9���F�'U MMP$�L 'N��� ��l�F�,���q�J�2�!_"J�1 �̟��/dl��!Z����a�?~���`�a�%��ic�0�q;��Pw7Q<^p�\l��r�[�jM#� ۦ�n���&%l"%�$oD)#i� xi����o��jg:�P{Rʠ�Pݢg�N�C�*��Tq^+x Z�(�[�AC�ߧ,��ų%%w.�6�&���5u�䠙lYn�9���Ro��>B��(����KW�B�:ٶ0m""�t]#-��o ��;������+1�#%`S'[�,����d��I)�f4��C�����2E��e� �j'���*�kDk0�q�a\v���Z ����U���=x&� a�Ţ��gK2����<��M1�0��g����wH�*�?x8���_��7����b�t�,��RW�,+_�GZԀP����S��%�w����^�2��W���*q ,�S7�"��r��� �rC���?F��)�F�O�[ �@��E}� ��Y��b�am4�-O/�b�N���5��_�)�4Pq�눰��kB�Va�pH �@ �$OV�b�0��:��i��;�t�PM>�GB��c�h� �䅲�v��+�/@�(F(��q�G�����)~F�j����ȟ{�@ܦRn̓�_0n�@������� :r�:�!�v_Q�btCU��;Ua�E�Hs�R�DM��� "M�� �*�A3~���A:AE��;A]TY Y�K$R�<}"A���wc��|$�_Dd������0��UTD�e!�@�y���(U)�_�{�Z�L}H�U�g��U n�)X�8$)񡜊�#W���gN����dN� �U�=���R��l���.yY� De��^#�C��+c���3�?K-0�8SAD�-d�l����M�(*��(�MDQ1��)�1^� (���mϼ$"Ad>���Ĉ4ADB���$�D�e�5�Me:� �t��iޢg�_/� xІ����2�N��Q���c _b�����"��Z��r0J��?i--�״QNӪ�C�@B�w�FV���~�����=�������=�؏z��M��4w:]Кz��7�G� �m-��e��t�������o�O�G��fs9��K�����Fp��>�|�z���gg�l��iܲr��}! 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