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The development of hardware-based cognitive computing systems critically hinges upon the integration of memristor devices capable of versatile weight expression across a spectrum of resistance levels while preserving consistent electrical properties. This investigation aims to explore the practical implementation of a digit recognition system utilizing memristor devices with minimized weighting levels. Through the process of weight quantization for digits represented by 25 or 49 input signals, the study endeavors to ascertain the feasibility of digit recognition via neural network computation. The integration of memristor devices into the system architecture is poised to streamline the representation of the resistors required for weight expression, thereby facilitating the realization of neural-network-based cognitive systems. To minimize the information corruption in the system caused by weight quantization, we introduce the concept of “weight range” in this work. The weight range is the range between the maximum and minimum values of the weights in the neural network. We found that this has a direct impact on weight quantization, which reduces the number of digits represented by a weight below a certain level. This was found to help maintain the information integrity of the entire system despite the reduction in weight levels. Moreover, to validate the efficacy of the proposed methodology, quantized weights are systematically applied to an array of double-layer neural networks. This validation process involves the construction of cross-point array circuits with dimensions of 25 × 10 and 10 × 10, followed by a meticulous examination of the resultant changes in the recognition rate of randomly generated numbers through device simulations. Such endeavors contribute to advancing the understanding and practical implementation of hardware-based cognitive computing systems leveraging memristor devices and weight quantization techniques.

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.

Matrix operations are nowadays central in many Machine Learning techniques, including in particular Deep Neural Networks (DNNs), whose core of any inference is represented by a sequence of dot product operations. An increasingly emerging problem is how to efficiently engineer their storage and operations. In this article we propose two new lossless compression schemes for real-valued matrices, supporting efficient vector-matrix multiplications in the compressed format, and specifically suitable for DNNs compression. Exploiting several recent studies that use weight pruning and quantization techniques to reduce the complexity of DNN inference, our schemes are expressly designed to benefit from both, that is from input matrices characterized by low entropy. In particular, our solutions are able to take advantage from the depth of the model, and the deeper the model, the higher the efficiency. Moreover, we derived space upper bounds for both variants in terms of the source entropy. Experiments show that our tools favourably compare in terms of energy and space efficiency against state-of-the-art matrix compression approaches, including Compressed Linear Algebra (CLA) and Compressed Shared Elements Row (CSER), the latter explicitly proposed in the context of DNN compression.

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.

Robot vacuum cleaners have gained widespread popularity as household appliances. One significant challenge in enhancing their functionality is to identify and classify small indoor objects suitable for safe suctioning and recycling during cleaning operations. However, the current state of research faces several difficulties, including the lack of a comprehensive dataset, size variation, limited visual features, occlusion and clutter, varying lighting conditions, the need for real-time processing, and edge computing. In this paper, I address these challenges by investigating a lightweight AI model specifically tailored for robot vacuum cleaners. First, I assembled a diverse dataset containing 23,042 ground-view perspective images captured by robot vacuum cleaners. Then, I examined state-of-the-art AI models from the existing literature and carefully selected three high-performance models (Xception, DenseNet121, and MobileNet) as potential model candidates. Subsequently, I simplified these three selected models to reduce their computational complexity and overall size. To further compress the model size, I employed post-training weight quantization on these simplified models. In this way, our proposed lightweight AI model strikes a balance between object classification accuracy and computational complexity, enabling real-time processing on resource-constrained robot vacuum cleaner platforms. I thoroughly evaluated the performance of the proposed AI model on a diverse dataset, demonstrating its feasibility and practical applicability. The experimental results show that, with a small memory size budget of 0.7 MB, the best AI model is L-w Xception 1, with a width factor of 0.25, whose resultant object classification accuracy is 84.37%. When compared with the most accurate state-of-the-art model in the literature, this proposed model accomplished a remarkable memory size reduction of 350 times, while incurring only a slight decrease in classification accuracy, i.e., approximately 4.54%.

Mixed-signal and fully digital neuromorphic systems have been of significant interest for deploying spiking neural networks in an energy-efficient manner. However, many of these systems impose constraints in terms of fan-in, memory, or synaptic weight precision that have to be considered during network design and training. In this paper, we present quantized rewiring (Q-rewiring), an algorithm that can train both spiking and non-spiking neural networks while meeting hardware constraints during the entire training process. To demonstrate our approach, we train both feedforward and recurrent neural networks with a combined fan-in/weight precision limit, a constraint that is, for example, present in the DYNAP-SE mixed-signal analog/digital neuromorphic processor. Q-rewiring simultaneously performs quantization and rewiring of synapses and synaptic weights through gradient descent updates and projecting the trainable parameters to a constraint-compliant region. Using our algorithm, we find trade-offs between the number of incoming connections to neurons and network performance for a number of common benchmark datasets.

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.

With the refinement of tasks in artificial intelligence, bringing in exponential level increments in computation cost and storage. Therefore, the augment of computation resource for complicated neural networks severely hinders their applications on limited-power devices in recent years. As a result, there is an impending necessity to compress and accelerate the deep networks by special ways. Considering the different peculiarities of weight quantization and sparse regularization, in this paper, we propose a low rank sparse quantization (LRSQ) method to quantize network weights and regularize the corresponding structures at the same time. Our LRSQ can: 1) obtain low-bit quantized networks to reduce memory and computation cost and 2) learn a compact structure from complex convolutional networks for subsequent channel pruning which has significant reduction on FLOPs. In experimental sections, we evaluate the proposed method on several popular models such as VGG-7/16/19 and ResNet-18/34/50, and results show that this method can dramatically reduce parameters and channels of the network with slight inference accuracy loss. Furthermore, we also visualize and analyze the four-dimensional weight tensors, which shows the low rank and group-sparsity structure of it. Finally, we try pruning unimportant channels which are zero-channels in our quantized model, and finding even a little better precision than the standard full-precision network.

Due to controllable conductance and non-volatility, flexible memristors are regarded as a key enabler for building artificial neural network (ANN)-based learning algorithms in flexible and wearable systems. However, the existing flexible memristors are suffering from limited number of conductance values, issues limiting large-scale integration, and insufficient accuracy that cannot support accurate computation of ANN. In this paper, solutions are proposed for the three major challenges of the flexible memristor; the feasibility of a three-layer fully connected neural network on MNIST and a 13-layer convolutional neural network (CNN) on CIFAR-10 using the flexible memristor based on single-walled carbon nanotubes network/polymer composite and hydrophilic Al2O3 dielectric are studied. The evaluation result shows that in the fully connected neural network system, it is able to recognize MNIST with an accuracy above 90% after 4-bit quantization, 52.05% decrease in interconnection numbers in the circuit and up to 40% random error introduced, and in the CNN on CIFAR-10, the system can retain an accuracy above 86% with less than 4% accuracy loss after 5-bit quantization, 59.34% decrease in interconnection numbers in the circuit and up to 40% random error injected.

A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artificial neural network (ANN) are transferred to synaptic devices. However, it is time-consuming to map all the synaptic weights as the scale of the neural network increases. In this paper, we propose a method for quantized weight transfer using spike-timing-dependent plasticity (STDP) for hardware-based SNN. STDP is an online learning algorithm for SNN, but we utilize it as the weight transfer method. Firstly, we train SNN using the Modified National Institute of Standards and Technology (MNIST) dataset and perform weight quantization. Next, the quantized weights are mapped to the synaptic devices using STDP, by which all the synaptic weights connected to a neuron are transferred simultaneously, reducing the number of pulse steps. The performance of the proposed method is confirmed, and it is demonstrated that there is little reduction in the accuracy at more than a certain level of quantization, but the number of pulse steps for weight transfer substantially decreased. In addition, the effect of the device variation is verified.

The workloads of Convolutional Neural Networks (CNNs) exhibit a streaming nature that makes them attractive for reconfigurable architectures such as the Field-Programmable Gate Arrays (FPGAs), while their increased need for low-power and speed has established Application-Specific Integrated Circuit (ASIC)-based accelerators as alternative efficient solutions. During the last five years, the development of Hardware Description Language (HDL)-based CNN accelerators, either for FPGA or ASIC, has seen huge academic interest due to their high-performance and room for optimizations. Towards this direction, we propose a library-based framework, which extends TensorFlow, the well-established machine learning framework, and automatically generates high-throughput CNN inference engines for FPGAs and ASICs. The framework allows software developers to exploit the benefits of FPGA/ASIC acceleration without requiring any expertise on HDL development and low-level design. Moreover, it provides a set of optimization knobs concerning the model architecture and the inference engine generation, allowing the developer to tune the accelerator according to the requirements of the respective use case. Our framework is evaluated by optimizing the LeNet CNN model on the MNIST dataset, and implementing FPGA- and ASIC-based accelerators using the generated inference engine. The optimal FPGA-based accelerator on Zynq-7000 delivers 93% less memory footprint and 54% less Look-Up Table (LUT) utilization, and up to 10× speedup on the inference execution vs. different Graphics Processing Unit (GPU) and Central Processing Unit (CPU) implementations of the same model, in exchange for a negligible accuracy loss, i.e., 0.89%. For the same accuracy drop, the 45 nm standard-cell-based ASIC accelerator provides an implementation which operates at 520 MHz and occupies an area of 0.059 mm 2 , while the power consumption is ∼7.5 mW.