For convolutional layers, the kernels allow for sparsity in input and output feature dimensions; the connectivity is still dense in the case where the weights of the neural network are sparse, i. Sep 16, 2019 · Non-contiguous and categorical sparse feature data are widely existed on the Internet. On the other hand, our method performs both weight pruning and network training simultaneously. In this work, we propose a new automatic pruning method Apr 3, 2020 · Deep neural network compression is important and increasingly developed especially in resource-constrained environments, such as autonomous drones and wearable devices. In this paper, we Sparse Neural Networks. With this simple, three-step sparse retraining workflow, you can generate sparse neural networks that match the baseline accuracy, and TensorRT 8. A strategy that combines the idea of neural network structure search with a pruning algorithm to alleviate the difficulty of training or performance degradation of the sub-networks after pruning and the forgetting of the weights of the original lottery ticket hypothesis is proposed. S. As an alternative to group lasso that encourages feature sharing, exclusive sparsity [ 104 ] enforces the model weight parameters to compete for features, making the features Variational Spike-and-Slab Neural Networks (Louizos, 2015) is yet another Bayesian interpre-tation of Binary Dropout that allows for tuning of individ-ual dropout rates and also leads to a sparse solution. . The size of the sparse weight matrix B is k ×n, which transforms k-dimensional features into Jan 7, 2021 · New methods for penalizing the weights in deep neural networks have been developed which regularize the network parameters by utilizing sparse representation based feature selection methods. Un-fortunately, this procedure does not scale well with model width and depth. Han et al. edu 1 Introduction The success of deep neural networks (NNs) in recent years has come with their growing size and overparameterization [1, 2]. Graph Neural Networks. See full list on arxiv. The benefits of combining sparse activation with sparse weights are huge. Q i=1S. We suggest a multiobjective perspective on the training of neural networks by treating Jul 20, 2021 · We developed 2:4 fine-grained structured sparsity and built support directly into NVIDIA Ampere Architecture Sparse Tensor Cores. They present a mechanism for building neural networks that achieve high performance when each weight in the network has the same shared value. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science ANN models end up with weights histograms that peak around zero11–13. Fine-grained sparse weights reduce memory by up to 16×. ,2020;Stock et al. Jan 31, 2021 · The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Recently, most of the dedicated neural network accelerators are beginning to exploit the sparsity of neural network models for performance boost and energy saving. 2. Expand. 1109/ASAP57973. Feb 5, 2024 · Figure 3: Dynamic sparsity algorithms improve the optimization of sparse neural networks by leveraging updates during training. CNNs are increasingly being used in many real-world applications. Download conference paper PDF. Pruning was mainly done by using magnitude as an approximation for saliency to determine Mar 6, 2020 · Sparse neural networks (SNNs) are emerging as a promising technique for resource-limited intelligent embedded systems because of the compact model size and the un compromised accuracy. We’re releasing highly optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Tibshirani. Jan 8, 2019 · Adaptive Spiking Neural Network conversion schematic. More precisely, our algorithm, called SparseProp, is general in the sense that 1) it applies to arbitrary sparsity Jul 1, 2023 · SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights. output of previous layer), may be expressed as: z = a. A convolutional neural network with sparse representation. this is a sparse weight update that only alters the values in a single column of matrix \({M Nov 1, 2019 · Our objective is to construct a sparse neural network with fewer parameters and comparable or even better performance than the dense model. Nov 13, 2023 · Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is computed on the fly during inference to generate the target model's weights. Chenglong Zou. The monotonically growing threshold ( ) causes more blocks to be pruned as training progress. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We’ve used them to attain state-of-the-art results in text sentiment analysis and generative modeling of text and images. Figure sourced from the RigL paper. It adopts a S. TLDR. However, it cannot prune the weights and generate a sparse structure in neural networks [29], [30]. Mar 9, 2021 · One way to optimize a model is through use of sparse neural networks [1, 2, 3], which have a significant fraction of their weights set to zero. Basically, we can easily and largely reduce the number of weights of a trained deep model by adopting a widely used model compression technique, e. Feb 4, 2021 · However, the RVFL network did not consider the topological relationship between samples. This makes it critical to efficiently compress, store and communicate Mar 1, 2023 · The l 2-norm regularization is widely known as the “weight decay” for neural networks, which controls the magnitude of weights and improves the generalization ability of the model [27], [28]. Block-Sparse Recurrent Neural Networks. So, inspired by the pruning method for CNN, the progressive sparsity is introduced to apply sparsity only on fine-tuning phase for such tasks without too much accuracy loss. 6K for the dense net. Jan 26, 2021 · Zhou et al. 3. Ming Wen1, Yixi Xu2, Yunling Zheng1, Zhouwang Yang1and Xiao Wang2 1University of Science and Technology of China and2Purdue University. The weights are usually stored in the form of crossbars. In this paper, we propose a factorized weight interaction neural network (INN) with a new network structure called weight-interaction layer to learn patterns from feature interactions and domain neural network (HTNN) [10] was proposed as a frame-work to learn structured sparse-orthogonal weights where convolutions are replaced by element-wise multiplications. Weight Agnostic Neural Networks In Weight Agnostic Neural Networks (WANNs) [6], Gaier and Ha question if an architecture alone may en-code the solution to a problem. SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge Mahdi Nikdan* 1 Tommaso Pegolotti* 2 Eugenia Iofinova 1Eldar Kurtic Dan Alistarh1 3 Abstract We provide an efficient implementation of the backpropagation algorithm, specialized to the case where the weights of the neural network be-ing trained are Jul 22, 2022 · In this paper, we introduce a novel method of neural network weight compression. Given such high-dimensional feature vectors, model size may become huge and require huge amounts of RAM. 9V, peak energy efficiency of 3. The pruning routine can be either single-shot [22] or iterative [12,13,36,38]. However, these strategies remain secondary to most learning approaches and suffer from time and computational intensive procedures. For example, a FC network with x=14 has the same number of weights (around 11000) as a 50% dense network with x=28, a 22% dense network with x=56, and a 10% dense network with x=112. We show that this approach can be Jul 10, 2019 · Sparse Networks from Scratch: Faster Training without Losing Performance. Convolutional neural networks (CNNs) achieved great success on various tasks in recent years. In this paper, we introduce a novel method of neural network weight compression. Our new kernels allow efficient usage of block-sparse weights in fully connected and convolutional layers, as illustrated in the middle figure. Conference: 2022 IEEE International Symposium on Circuits and Systems Atlas WangAssistant Professor, Electrical and Computer EngineeringThe University of Texas at AustinAbstract: A sparse neural network (NN) has most of its pa Mar 9, 2018 · ArXiv. Zhenhui Dai. Oct 6, 2019 · Same with sparse model representation. Jan 1, 2016 · Pre-training-based initialization of deep neural network (DNN) weights. We use projected gradient descent methods to find quantized and sparse factorization of the weight tensors. 4× lower energy. Traditional feature selection algorithms often grapple with delineating non-linear relationships between features and responses. 0 accelerates them by default. To build a machine learning system with these data, it is important to properly model the interaction among features. These sparse neural networks have 26. We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. Most of these methods choose the Since sparse neural networks usually contain many zero weights, these unnecessary network connections can potentially be eliminated without degrading network performance. 2023. Mar 23, 2024 · The sparse deep neural network is built using the uniform linear array as an example, and the weighted vector of the array is obtained by training to actualize the necessary DM function. On the other hand, in neuron pruning, we remove entire neurons along with their connections, which leads to a smaller, less complex network. Oct 25, 2023 · The infinitely wide neural network has been proven a useful and manageable mathematical model that enables the understanding of many phenomena appearing in deep learning. A similar strategy is followed by Collins and Kohli [5]. Similarly to their biological counterparts, sparse networks generalize just as well, if not better than, the original dense networks. For more information, see Learning Both Weights and Connections for Efficient Neural Networks. Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. SWAT is more computation and memory-efficient than conventional training. ,2021;Fan et al. Although the initial values of these weights Oct 1, 2017 · This paper is an extended version of "Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations," presented in IEEE International Workshop on Signal block to zeros. 2015; Louizos, Welling, and Kingma 2018). Kingma. In a high-dimensional sparse vector, it would be nice to encourage weights to drop to exactly 0 where possible. Consider a dense layer whose weight matrix W 2RD0Dis expressed as a product of sparse matrices Q. , pruning. May 2, 2024 · We implement this kind of model in a neural network called the Sparse Quantized Hopfield Network. This work releases highly optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights, which improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. With brainspired dynamics - and spike-based communication, SNN is supposed to be a more energy-efficient neural network than existing artificial neural network (ANN). In order to further improve the robustness of RVFL, we propose Sparse Laplacian regularized RVFL neural network with L 2,1-norm (SLapRVFL). YQ i=1. Moreover The most popular approaches for learning a sparse neural networks are greedy pruning methods, which generally train a dense neural network, perform a prun-ing routine, and retrain the sparse network. In the context of NNs, things that may be described as sparse or dense include the activations of units within a particular layer, the weights, and the data. Jul 21, 2023 · In this paper, a spiking neural network accelerator with sparse spike and weight is presented, using ping-pong architecture to improve system data throughput. Section4 describes the methodology for training neural networks to have 2:4 weight sparsity so that inference can be accelerated. The paper theoretically explains the work procedure of the proposed network model. Conference: 2023 IEEE 34th International Conference on In Section 2 we explain how Bregman iterations can be incorporated into the training of sparse neural networks, derive and discuss variants of the proposed Algorithm 1: LinBreg, an inverse scale space algorithm for training sparse neural networks by successively adding weights whilst minimizing the loss. If the output of a layer is 20% sparse, and the weights to the next layer are also 20% sparse, the number of non-zero multiplications is only around 4%. A. i. On-chip STDP trains RBMs to de-noise MNIST digits and to reconstruct natural scene In the rest of this paper, we denote sparse matrix multipli-cation as C = A×B, where B is the sparse weight matrix, A and C are the input and output dense matrix (as shown in Figure2), following the conventional representation of GEMM in DNNs. 00015. We accomplish this by developing sparse momentum, an algorithm which Sep 1, 2023 · In this paper, we overturn back-propagation and combine the sparse network optimization problem and the network weight optimization problem using a non-convex optimization method, namely Simulated Annealing. contain a significant fraction of zero values, and show its potential for practical speedups in common edge training scenarios, for both vision and language tasks. Recent ral networks. Preliminaries. In this work, we propose a novel CNN training algorithm called Sparse Weight Activation Training (SWAT). Due to the unstructured pruning and compression in model training, the Tensor Cores, and Sparse Tensor Core operation are detailed in Section3. 8pJ/SOP at 525mV, and 2. More precisely, our algorithm, called SparseProp, is general in the sense that 1) it applies to arbitrary sparsity wild within neural network models. The objective function is May 1, 2022 · Assuming a predetermined network structure, these probabilities depend on the decision variables w ∈ R N (weights of the neural network) and that y d is defined on a probability space (with probability measure independent of w) for which we assume that i. Pruning is widely used to elimi-nate the weight redundancy in over-parameterized DNNs by removing weights with less importance (Collins and Kohli 2014; Han et al. 2GSOP/s at 0. Computer Science. However, existing sparsity-aware accelerators Figure 1: Visualization of random dense and random block-sparse weight matrices, where white indicates a weight of zero. Given a dense network θ l ∈Rd l with a dimension of d l in each layer l∈{1,,L}, binary mask m l ∈{0,1}d l yielding a sparse Neural Networks with sparse weights θ l⊙m l. The different sparsity levels follow a hierarchical structure such that the locations and values of the non-zero weights of the more-sparse sub-model is a subset of the less-sparse sub-model. We use projected gradient descent methods to find quantized and sparse factorization of the May 1, 2022 · In addition to the neural network’s parameters, many other components and hyperparameters are to be selected and fine-tuned when designing and training neural networks, e. With brain-spired dynamics and spike-based communication, SNN is supposed to be a more energy-efficient neural network than existing artificial neural network (ANN). Because group Lasso can effectively zero out all weights in some groups [14][15], we adopt it in our SSL. In Section5, we provide an empirical study on a variety of popular tasks and neural network architectures highlighting the universality Dec 6, 2017 · We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. 4K weights as compared to 70. Equivalences Between Sparse Models and Neural Networks. 1;1-WEIGHT NORMALIZATION. We are going to derive the number of trainable weights without weight sharing and also with weight sharing, within the first convolutional layer of two popular CNN architectures: LeNet and AlexNet . We present some observations about neural networks that are, on the one hand, the result of fairly trivial algebraic manipulations, and on the other hand, potentially noteworthy and deserving of further study. Yet, sparse activations, while omnipresent in Spiking neural network (SNN) is a promising generation of neural networks inspired by biology, which has the advantages of high energy efficiency and hardware-friendly. Jan 7, 2020 · Neural network training is computationally and memory intensive. Deep learning models have a lot of redundancies, and in many cases you can get rid of a lot of weights by preserving the quality of results, as discussed for example by James Kwork in this talk on Compressed Deep Neural Networks. Jul 18, 2022 · Sparse vectors often contain many dimensions. The convolutional weight tensors are reshaped into matrices, then K-means methods is applied Jul 26, 2023 · Sparsity has probably been the first structure applied to neural networks. d. We show that this approach can be Oct 1, 2019 · The overall density of these pre-defined sparse networks is set such that for different values of x, different networks have the same number of weights. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can also affect network convergence. 2 Sparse neural networks throughout training. For the output node, it must be connected to both the input and the hidden layer. While deep neural networks excel in capturing such non-linearities, their inherent “black-box” nature detracts Jan 23, 2024 · On-device training for deep neural networks (DNN) has become a trend due to various user preferences and scenarios. The outcomes demonstrate the viability of the suggested design. We present a new class of transform domain deep neural networks (DNNs), where convolution operations are replaced by element-wise multiplications in Jul 3, 2023 · Sparse pretraining requires more data and is more time-consuming. The SNN skips zero-valued activations for up to 9. Their applications to low power and low cost hardware platforms, however, have been often limited due to extensive complexity of convolution layers. In ANNs, every layer performs a series of weighted sums of the inputs as each analog input is multiplied by its analog weight. Sparse RNNs are easier to deploy on devices and high-end May 21, 2022 · The sparse convolutional neural network (CNN) is created by tracing 1024 random paths. 3 BACKGROUND 3. We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in SPARSE DEEP NEURAL NETWORKS USING L. In this work, we propose a novel CNN training algorithm Sparse Weight Activation Training (SWAT). Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. “Signs non-trainable” means that signs are kept fixed after initialization, while training only the weight magnitudes. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e. To make better use of the temporal sparsity of spikes and spatial sparsity of weights in SNN, this paper presents a sparse SNN accelerator. Current training accelerators usually ignore the special computation property of Jul 22, 2022 · In this paper, we introduce a novel method of neural network weight compression. The block-sparse mask is multiplied with the weights to generate block-sparse weight matrix. Xiaoxin Cui. Full size table. The L 2,∞ normalization of the weight matrices is used to enhance the robustness and accuracy of the deep neural network (DNN) with Relu as activation functions and it is shown that it leads to large dihedral angles between two adjacent faces of the DNN function graph and hence smoother DNN functions, which reduces over-fitting of theDNN. This paper proposes the hybrid of a CNN model and sparse representation. To reduce the inference delay, the proposed accelerator supports the decoupling of calculation of membrane potential and leaky integrate-and-fire (LIF) dynamics computing in the Dec 1, 2023 · The efficacy of feature selection methods in dimensionality reduction and enhancing the performance of learning algorithms has been well documented. ,2019;Wu et al. Published2017. samples can be Quantized sparse weight decomposition for neural network compression tion approach for compression of weights of convolutional and fully connected layers ((Gong et al. , 2016)). These are Accelerator with On-chip Sparse Weight Abstract—Spiking neural networks (SNNs) have widely drew attention of recent research. Jun 30, 2022 · Analogue memory-based deep neural networks provide energy-efficiency and per-area throughput gains relative to state-of-the-art digital counterparts such as graphics processing units. The vector w 2RQ is used as a shorthand for the column-vector concatenation of all adaptable parameters of the network. Further, our method has considerably less number of hyper-parameters to deter- Our general idea is to replace the weight matrix of every layer with a product of Q sparse matrices S. (application/pdf) Download. Neural network training is computationally and memory intensive. , the type of layers, the batch size, the amount of dropout or the regularization and the learning rate. WEIGHT-LEVEL REGULARIZATION FOR NEURAL NETWORKS Let us denote by y = f(x;w) a generic deep neural network, taking as input a vector x 2Rd, and returning a vector y 2Ro after propagating it through H hidden layers. You can remove those redundant weights and use sparser solution, that needs Aug 30, 2019 · The first major paper advocating sparsity in neural networks dates back from 1990, written by LeCun and colleagues while working at AT&T Bell Laboratories. Aug 9, 2019 · A sparse matrix is a matrix that is comprised of mostly zero values. SWAT A 4096-neuron, 1M-synapse SNN in 10nm FinFET CMOS achieves a peak throughput of 25. In the next section, the most important structured sparsity inducing feature regularization methods are detailed. Jan 12, 2021 · However, a critical issue for realization of PCM-based memristor neural network is that even if the weights represented by the resistance are precisely tuned to the ideal weights, its most Sep 8, 2023 · For example, neural networks trained by weight pruning are highly clusterable 22, and artificial neural networks initialized with random sparse topologies tend to evolve to heavy-tailed topologies the case where the weights of the neural network are sparse, i. further found that the sign of the initialization of “winning tickets” actually results in a good performance, not the corresponding weight values. Sparse matrices are distinct from matrices with mostly non-zero values, which are referred to as dense matrices. [9] create sparse networks by al-ternating between weight pruning and network training. This results in a sparse representation of weight matrices. Sparsifying the parameters, often achieved through weight pruning, has been identified as a powerful technique to compress the number of model parameters and reduce the computational operations of neural networks. isik@stanford. e. July 2023. In an HTNN, two or more kernels in different transform domains share a multiplier without conflict as the non-zero weight positions are strictly orthogonal to each other. A matrix is sparse if many of its coefficients are zero. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can affect network convergence. This method can complete network training under the premise of controlling the amount of parameters. (Left) During training of a deep ANN, the output of a convolutional or fully connected layer is passed through the ASN transfer function. 2. Their approaches used information from second-order derivatives in order to remove unimportant weights from the network. To make better use of the temporal sparsity of Dec 17, 2020 · Knowledge-Based Systems. For example, RigL [2] utilizes weight and gradient magnitudes to jointly optimize model parameters and connectivity. May 2022. A weight of exactly 0 First, sparse partial least squares regression is used to replace least squares estimation for network output weights calculation, which not only can effectively solve the multicollinearity problem caused by correlations, but also enable variable selection to improve the performance and interpretability. Spiking neural networks (SNNs) have widely drew attention of recent research. Sparsity is a technique to reduce compute and memory requirements of deep learning models. The DNN training process consists of three phases, feedforward (FF), backpropagation (BP), and weight gradient (WG) update. 44% loss for top-5 accuracy in VGG-16 ) while ours does not incur any accuracy loss because of the lossless compression of IFMs. , ‘ 2-norm; and R g() is the structured sparsity regularization on each layer. We propose a training and inference methodology that creates sparse neural network models with multiple sparsity levels. Published 2021. An Event-Driven Spiking Neural Network Accelerator with On-Chip Sparse Weight. g. Obtaining sparse weight matrices has for example been addressed by Hassibi and Stork and Le Cun et al. R. i, then the ouput of the layer given an input vector x 2RD(e. block to zeros. DOI: 10. 3μW/neuron operation at 450mV. The interest in sparsity arises because its exploitation can lead to enormous computational savings and Neural Network Weights (Theory) Berivan Isik berivan0 Department of Electrical Engineering Stanford University berivan. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is Aug 20, 2019 · The weight initialization technique you choose for your neural network can determine how quickly the network converges or whether it converges at all. In SNN, the input is not encoded as real-valued activations but as sequences of binary spikes. One example is the convergence of random deep networks to Gaussian processes that allows a rigorous analysis of the way the choice of activation function and network weights impacts the training dynamics. 2021. We combine the CNN with predefined filters of wavelet and shearlet transform. II. Feb 24, 2021 · As a result, the groups of weights are pruned when their ℓ 2 norms are negligible, resulting in a highly compact network compared to element-sparse networks. Recently, some works of dynamic sparse training have emerged, allowing train sparse neural networks from the beginning. 9937521. Here W represents the collection of all weights in the DNN; E D(W) is the loss on data; R() is non-structured regularization applying on every weight, e. 1109/ISCAS48785. Author (s) Yisong Kuang. , convolutional or linear). Gray, Alec Radford, Diederik P. i. Left: The autoencoder (AE) architecture for the layer-wise pre-training; Right: the stacked AE (SAE) architecture between the input and the second hidden layers using the output of the first hidden layer as an input to the AE; Middle: SAE pre-training-based multilayer networks. In general, this is a desirable quality as it not only reduces the model size via compression, but also makes it possible to skip a significant fraction of multiply-add operations, thereby speeding up Nov 29, 2021 · In addition, due to the data losses during the weight compression, there is an inevitable accuracy loss (0. ,2021) are the state-of-the-art in sparse training methods for Deep Neural Networks (DNNs). Creating a feature cross results in even more dimensions. Computer Science, Mathematics. 2022. Oct 4, 2018 · The usage of these terms in the context of neural networks is similar to their usage in other fields. Yi Zhong. Jul 22, 2022 · Abstract and Figures. DST methods typically learn unstructured masks resulting in 85–95% fewer weights than dense models, while maintaining dense-like general- Sep 1, 2023 · In weight pruning, we remove individual connections in the neural network, setting the corresponding weights to zero. WG takes about one-third of the computation in the whole training process. Figure1depicts the process of generating a block-sparse mask from a weight matrix for a given threshold. May 28, 2022 · An Event-driven Spiking Neural Network Accelerator with On-chip Sparse Weight. In neural networks with multiple layers, let W (l) represent the weight matrix of l th layer. Abstract: Deep neural networks (DNNs) have recently demonstrated an excellent performance on many challenging tasks. The sparsity level is the fraction of the weights that are zero-valued, calculated as s= 1 − P lP ∥m l∥ 0 l d l. At the time, post-pruning neural networks to compress trained models was already a popular approach. 1 Convolutional neural networks. Pruning for Spiking Neural Networks Recently, there has been a significant growing interest in exploring spiking neural networks (SNNs) as the new gener-ation of low-power deep neural networks under the context of edge machine learning [26], [27]. One of the main groups of SNN research focuses on the compression of the size of Jun 16, 2020 · The goal of this section is to bring to light the benefits of weight sharing that occurs within convolutional neural networks. ,2014;Martinez et al. Therefore, well-designed sparse neural networks have the potential to significantly reduce the number of floating-point operations (FLOPs) and computational resources. Sparsity can reduce the memory footprint of regular networks to fit mobile devices, as well as shorten Feb 9, 2023 · We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. The Jul 22, 2022 · In this paper, we introduce a novel method of neural network weight compression. Sparsification of neural networks is becoming increasingly important under the requirement to deploy such models to resource-limited devices. org Oct 30, 2020 · But another relevant theme is neural networks with sparse activations. Aug 31, 2020 · Overparameterization and overfitting are common concerns when designing and training deep neural networks, that are often counteracted by pruning and regularization strategies. Dynamic Sparse Training (DST) methods such as RigL (Evci et al. psphlpskocpepyopsocg