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Artificial intelligence (AI) plays a critical role in the operation of robot vacuum cleaners, enabling them to intelligently navigate to clean and avoid indoor obstacles. Due to limited computational resources, manufacturers must balance performance and cost. This necessitates the development of lightweight AI models that can achieve high performance. Traditional uniform weight quantization assigns the same number of levels to all weights, regardless of their distribution or importance. Consequently, this lack of adaptability may lead to sub-optimal quantization results, as the quantization levels do not align with the statistical properties of the weights. To address this challenge, in this work, we propose a new technique called low bitwidth strong non-uniform quantization, which largely reduces the memory footprint of AI models while maintaining high accuracy. Our proposed non-uniform quantization method, as opposed to traditional uniform quantization, aims to align with the actual weight distribution of well-trained neural network models. The proposed quantization scheme builds upon the observation of weight distribution characteristics in AI models and aims to leverage this knowledge to enhance the efficiency of neural network implementations. Additionally, we adjust the input image size to reduce the computational and memory demands of AI models. The goal is to identify an appropriate image size and its corresponding AI models that can be used in resource-constrained robot vacuum cleaners while still achieving acceptable accuracy on the object classification task. Experimental results indicate that when compared to the state-of-the-art AI models in the literature, the proposed AI model achieves a 2-fold decrease in memory usage from 15.51 MB down to 7.68 MB while maintaining the same accuracy of around 93%. In addition, the proposed non-uniform quantization model reduces memory usage by 20 times (from 15.51 MB down to 0.78 MB) with a slight accuracy drop of 3.11% (the classification accuracy is still above 90%). Thus, our proposed high-performance and lightweight AI model strikes an excellent balance between model complexity, classification accuracy, and computational resources for robot vacuum cleaners. [ABSTRACT FROM AUTHOR]

The state-of-the-art performance for several real-world problems is currently reached by deep and, in particular, convolutional neural networks (CNN). Such learning models exploit recent results in the field of deep learning, leading to highly performing, yet very large neural networks with typically millions to billions of parameters. As a result, such models are often redundant and excessively oversized, with a detrimental effect on the environment in terms of unnecessary energy consumption and a limitation to their deployment on low-resource devices. The necessity for compression techniques able to reduce the number of model parameters and their resource demand is thereby increasingly felt by the research community. In this paper we propose the first extensive comparison, to the best of our knowledge, of the main lossy and structure-preserving approaches to compress pre-trained CNNs, applicable in principle to any existing model. Our study is intended to provide a first and preliminary guidance to choose the most suitable compression technique when there is the need to reduce the occupancy of pre-trained models. Both convolutional and fully-connected layers are included in the analysis. Our experiments involved two pre-trained state-of-the-art CNNs (proposed to solve classification or regression problems) and five benchmarks, and gave rise to important insights about the applicability and performance of such techniques w.r.t. the type of layer to be compressed and the category of problem tackled. [ABSTRACT FROM AUTHOR]

To analyze the effect of the intrinsic variations of the memristor device on the neuromorphic system, we fabricated 32 × 32 Al2O3/TiO x -based memristor crossbar array and implemented 3 bit multilevel conductance as weight quantization by utilizing the switching characteristics to minimize the performance degradation of the neural network. The tuning operation for 8 weight levels was confirmed with a tolerance of ±4 μ A (±40 μ S). The endurance and retention characteristics were also verified, and the random telegraph noise (RTN) characteristics were measured according to the weight range to evaluate the internal stochastic variation effect. Subsequently, a memristive neural network was constructed by off-chip training with differential memristor pairs for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. The pre-trained weights were quantized, and the classification accuracy was evaluated by applying the intrinsic variations to each quantized weight. The intrinsic variations were applied using the measured weight inaccuracy given by the tuning tolerance, RTN characteristics, and the fault device yield. We believe these results should be considered when the pre-trained weights are transferred to a memristive neural network by off-chip training. [ABSTRACT FROM AUTHOR]

Recently, various memory devices have been actively studied as suitable candidates for synaptic devices, which are important memory and computing units in neuromorphic systems. One of the ways to manage these devices is off-chip training, where it is essential to transfer the pretrained weights accurately. Previous studies, however, have a few limitations, such as a lack of consideration of program errors that occur during the transfer process. Although the smaller the program error, the higher the accuracy, the corresponding increase in the program time must be considered. To evaluate the practical applicability, we fabricated Al2O3/TiOx-based resistive random access memory (RRAM) and investigated the effect of program errors on program time and system degradation. It was confirmed that for smaller program errors, the program time was exponentially longer. Furthermore, we examined the effect of variation with respect to the number of quantized weight states (${N}_{state}$) through system-level simulation. We observed that the optimized ${N}_{state}$ varies depending on whether the program error is small or large. This result is meaningful as it experimentally shows the tradeoff between the program error, program time, and system performance. We expect it to be useful in the development of neuromorphic systems. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision. Unlike in prior designs, we propose a novel model compression approach based on Simonk-means, which is specifically designed to support a hardware acceleration scheme. First, we propose an extension algorithm named Simonk-means based on simple k-means. We use Simonk-means to cluster trained weights in convolutional layers and fully connected layers. Second, we reduce the consumption of hardware resources in data movement and storage by using a data storage and index approach. Finally, we provide the hardware implementation of the compressed CNN accelerator. Our evaluations on several classifications show that our design can achieve 5.27× compression and reduce 74.3% of the multiply–accumulate (MAC) operations in AlexNet on the FASHION-MNIST dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI) theory, particularly deep learning neural networks, robot vacuums equipped with AI power can automatically clean indoor floors by using intelligent programming and vacuuming services. To date, several deep AI models have been proposed to distinguish indoor objects between cleanable litter and noncleanable hazardous obstacles. Unfortunately, these existing deep AI models focus entirely on the accuracy enhancement of object classification, and little effort has been made to minimize the memory size and implementation cost of AI models. As a result, these existing deep AI models require far more memory space than a typical robot vacuum can provide. To address this shortcoming, this paper aims to study and find an efficient deep AI model that can achieve a good balance between classification accuracy and memory usage (i.e., implementation cost). In this work, we propose a weight-quantized SqueezeNet model for robot vacuums. This model can classify indoor cleanable litters from noncleanable hazardous obstacles based on the image or video captures from robot vacuums. Furthermore, we collect videos or pictures captured by built-in cameras of robot vacuums and use them to construct a diverse dataset. The dataset contains 20,000 images with a ground-view perspective of dining rooms, kitchens and living rooms for various houses under different lighting conditions. Experimental results show that the proposed deep AI model can achieve comparable object classification accuracy of around 93% while reducing memory usage by at least 22.5 times. More importantly, the memory footprint required by our AI model is only 0.8 MB, indicating that this model can run smoothly on resource-constrained robot vacuums, where low-end processors or microcontrollers are dedicated to running AI algorithms. [ABSTRACT FROM AUTHOR]

In this work, we present effective quantization schemes called DP-Nets for the compression and acceleration of deep neural networks (DNNs). A key ingredient is a novel dynamic programming (DP) based algorithm to obtain the optimal solution of scalar K-means clustering. Based on the approaches with regularization and quantization function, two weight quantization approaches called DPR and DPQ for compressing normal DNNs are proposed respectively. Accordingly, a technique based on DP-Nets for inference acceleration is presented. Experiments show that DP-Nets produce models with higher inference accuracy than recently proposed counterparts while achieving same or larger compression. They are also extended for compressing robust DNNs, and the relevant experiments show 16X compression of the robust ResNet-18 model with less than 3% accuracy drop on both natural and adversarial examples. The experiments with FPGA demonstrate that the technique for inference acceleration brings over 5X speedup on matrix–vector multiplication. • A dynamic programming (DP) method for the scalar K-means problem is proposed. • Two DP-based methods are proposed for DNN compression and acceleration. • The two methods are extended for compressing robust DNNs. • The technique for inference acceleration with compressed DNNs is validated on FPGA. [ABSTRACT FROM AUTHOR]

The deployment of large-scale Convolutional Neural Networks (CNNs) in limited-power devices is hindered by their high computation cost and storage. In this paper, we propose a novel framework for CNNs to simultaneously achieve channel pruning and low-bit quantization by combining weight quantization with Sparse Group Lasso (SGL) regularization. We model this framework as a discretely constrained problem and solve it by Alternating Direction Method of Multipliers (ADMM). Different from previous approaches, the proposed method reduces not only model size but also computational operations. In experimental section, we evaluate the proposed framework on CIFAR datasets with several popular models such as VGG-7/16/19 and ResNet-18/34/50, which demonstrate that the proposed method can obtain low-bit networks and dramatically reduce redundant channels of the network with slight inference accuracy loss. Furthermore, we also visualize and analyze weight tensors, which showing the compact group-sparsity structure of them. [ABSTRACT FROM AUTHOR]

Deep neural networks enable state-of-the-art accuracy on visual recognition tasks such as image classification and object detection. However, modern networks contain millions of learned connections, and the current trend is towards deeper and more densely connected architectures. This poses a challenge to the deployment of state-of-the-art networks on resource-constrained systems, such as smartphones or mobile robots. In general, a more efficient utilization of computation resources would assist in deployment scenarios from embedded platforms to computing clusters running ensembles of networks. In this paper, we propose a deep network compression algorithm that performs weight pruning and quantization jointly, and in parallel with fine-tuning. Our approach takes advantage of the complementary nature of pruning and quantization and recovers from premature pruning errors, which is not possible with two-stage approaches. In experiments on ImageNet, CLIP-Q (Compression Learning by In-Parallel Pruning-Quantization) improves the state-of-the-art in network compression on AlexNet, VGGNet, GoogLeNet, and ResNet. We additionally demonstrate that CLIP-Q is complementary to efficient network architecture design by compressing MobileNet and ShuffleNet, and that CLIP-Q generalizes beyond convolutional networks by compressing a memory network for visual question answering. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limitations on crossbar size (implementing dot product), the constrained number of conductance states in non-CMOS devices and the power budget. We present a sparse SNN topology where noncritical connections are pruned to reduce the network size, and the remaining critical synapses are weight quantized to accommodate for limited conductance states. Pruning is based on the power law weight-dependent spike timing dependent plasticity model; synapses between pre- and post-neuron with high spike correlation are retained, whereas synapses with low correlation or uncorrelated spiking activity are pruned. The weights of the retained connections are quantized to the available number of conductance states. The process of pruning noncritical connections and quantizing the weights of critical synapses is performed at regular intervals during training. We evaluated our sparse and quantized network on MNIST dataset and on a subset of images from Caltech-101 dataset. The compressed topology achieved a classification accuracy of 90.1% (91.6%) on the MNIST (Caltech-101) dataset with 3.1X (2.2X) and 4X (2.6X) improvement in energy and area, respectively. The compressed topology is energy and area efficient while maintaining the same classification accuracy of a 2-layer fully connected SNN topology. [ABSTRACT FROM AUTHOR]