22,434 research outputs found
Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-based SCA
Deep learning-based side-channel analysis (SCA) represents a strong approach for profiling attacks. Still, this does not mean it is trivial to find neural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to overfitting but could have insufficient capacity to model the data. On the other hand, large neural networks have sufficient capacity but can overfit and are more difficult to tune. This brings an interesting trade-off between simplicity and performance.
This work proposes to use a pruning strategy and recently proposed Lottery Ticket Hypothesis (LTH) as an efficient method to tune deep neural networks for profiling SCA. Pruning provides a regularization effect on deep neural networks and reduces the overfitting posed by overparameterized models. We demonstrate that we can find pruned neural networks that perform on the level of larger networks, where we manage to reduce the number of weights by more than 90% on average. This way, pruning and LTH approaches become alternatives to costly and difficult hyperparameter tuning in profiling SCA. Our analysis is conducted over different masked AES datasets and for different neural network topologies. Our results indicate that pruning, and more specifically LTH, can result in competitive deep learning models
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
Spiking neural networks (SNNs) have superb characteristics in sensory
information recognition tasks due to their biological plausibility. However,
the performance of some current spiking-based models is limited by their
structures which means either fully connected or too-deep structures bring too
much redundancy. This redundancy from both connection and neurons is one of the
key factors hindering the practical application of SNNs. Although Some pruning
methods were proposed to tackle this problem, they normally ignored the fact
the neural topology in the human brain could be adjusted dynamically. Inspired
by this, this paper proposed an evolutionary-based structure construction
method for constructing more reasonable SNNs. By integrating the knowledge
distillation and connection pruning method, the synaptic connections in SNNs
can be optimized dynamically to reach an optimal state. As a result, the
structure of SNNs could not only absorb knowledge from the teacher model but
also search for deep but sparse network topology. Experimental results on
CIFAR100 and DVS-Gesture show that the proposed structure learning method can
get pretty well performance while reducing the connection redundancy. The
proposed method explores a novel dynamical way for structure learning from
scratch in SNNs which could build a bridge to close the gap between deep
learning and bio-inspired neural dynamics
Bayesian sparsification for deep neural networks with Bayesian model reduction
Deep learning's immense capabilities are often constrained by the complexity
of its models, leading to an increasing demand for effective sparsification
techniques. Bayesian sparsification for deep learning emerges as a crucial
approach, facilitating the design of models that are both computationally
efficient and competitive in terms of performance across various deep learning
applications. The state-of-the-art -- in Bayesian sparsification of deep neural
networks -- combines structural shrinkage priors on model weights with an
approximate inference scheme based on stochastic variational inference.
However, model inversion of the full generative model is exceptionally
computationally demanding, especially when compared to standard deep learning
of point estimates. In this context, we advocate for the use of Bayesian model
reduction (BMR) as a more efficient alternative for pruning of model weights.
As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc
elimination of redundant model weights based on the posterior estimates under a
straightforward (non-hierarchical) generative model. Our comparative study
highlights the advantages of the BMR method relative to established approaches
based on hierarchical horseshoe priors over model weights. We illustrate the
potential of BMR across various deep learning architectures, from classical
networks like LeNet to modern frameworks such as Vision Transformers and
MLP-Mixers
Regularization-based Pruning of Irrelevant Weights in Deep Neural Architectures
Deep neural networks exploiting millions of parameters are nowadays the norm
in deep learning applications. This is a potential issue because of the great
amount of computational resources needed for training, and of the possible loss
of generalization performance of overparametrized networks. We propose in this
paper a method for learning sparse neural topologies via a regularization
technique which identifies non relevant weights and selectively shrinks their
norm, while performing a classic update for relevant ones. This technique,
which is an improvement of classical weight decay, is based on the definition
of a regularization term which can be added to any loss functional regardless
of its form, resulting in a unified general framework exploitable in many
different contexts. The actual elimination of parameters identified as
irrelevant is handled by an iterative pruning algorithm. We tested the proposed
technique on different image classification and Natural language generation
tasks, obtaining results on par or better then competitors in terms of sparsity
and metrics, while achieving strong models compression
Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning
Convolutional Neural Networks (CNNs) have a large number of parameters and
take significantly large hardware resources to compute, so edge devices
struggle to run high-level networks. This paper proposes a novel method to
reduce the parameters and FLOPs for computational efficiency in deep learning
models. We introduce accuracy and efficiency coefficients to control the
trade-off between the accuracy of the network and its computing efficiency. The
proposed Rewarded meta-pruning algorithm trains a network to generate weights
for a pruned model chosen based on the approximate parameters of the final
model by controlling the interactions using a reward function. The reward
function allows more control over the metrics of the final pruned model.
Extensive experiments demonstrate superior performances of the proposed method
over the state-of-the-art methods in pruning ResNet-50, MobileNetV1, and
MobileNetV2 networks
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