The vision chip is widely used to acquire and process images. It connects the image sensor directly with the vision processing unit (VPU) to execute the vision tasks. Modern vision tasks mainly consist of image signal processing (ISP) algorithms and deep neural networks (DNNs). However, the traditional VPUs are unsuitable for the DNNs, and the DNN processing units (DNPUs) cannot process the ISP algorithms. Meanwhile, only the CNNs and the CNN-RNN frameworks are used in the vision tasks, and few DNPUs are specifically designed for this. In this paper, we propose a heterogeneous architecture for the VPU with a hybrid accelerator for the DNNs. It can process the ISP, CNNs, and hybrid DNN subtasks on one unit. Furthermore, we present a sharing scheme to multiplex the hardware resources for different subtasks. We also adopt a pipelined workflow for the vision tasks to fully use the different processing modules and achieve a high processing speed. We implement the proposed VPU on the field-programmable gate array (FPGA), and several vision tasks are tested on it. The experiment results show that our design can process the vision tasks efficiently with an average performance of 22.6 giga operations per second/W (GOPS/W).

Vision processing chips have been widely used in image processing and recognition tasks. They are conventionally designed based on the image signal processing (ISP) units directly connected with the sensors. In recent years, convolutional neural networks (CNNs) have become the dominant tools for many state-of-the-art vision processing tasks. However, CNNs cannot be processed by a conventional vision processing unit (VPU) with a high speed. On the other side, the CNN processing units cannot process the RAW images from the sensors directly and an ISP unit is required. This makes a vision system inefficient with a lot of data transmission and redundant hardware resources. Additionally, many CNN processing units suffer from a low flexibility for various CNN operations. To solve this problem, this paper proposed an efficient vision processing unit based on a hybrid processing elements array for both CNN accelerating and ISP. Resources are highly shared in this VPU, and a pipelined workflow is introduced to accelerate the vision tasks. We implement the proposed VPU on the Field-Programmable Gate Array (FPGA) platform and various vision tasks are tested on it. The results show that this VPU achieves a high efficiency for both CNN processing and ISP and shows a significant reduction in energy consumption for vision tasks consisting of CNNs and ISP. For various CNN tasks, it maintains an average multiply accumulator utilization of over 94% and achieves a performance of 163.2 GOPS with a frequency of 200 MHz.

General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture. It processes GEMM in a compressed format and eliminates on-chip transfers of both weights and partial sums. Furthermore, to map the compressed GEMM of various sizes onto the accelerator, an adaptable mapping scheme is designed. However, the irregular sparsity of weight matrices makes it difficult to store them in local buffers with the compressed format; denser vectors can exceed the buffer capacity, while sparser vectors may lead to the underutilization of buffers. To address this complication, this work also proposes an offline sparsity-aware shuffle strategy for weights, which balances the utilization of distributed buffers and minimizes buffer waste. Finally, a low-cost sparse computing method is applied to the WOS dataflow with globally shared inputs to achieve high computing throughput. Experiments with an FPGA show that the proposed accelerator achieves 1.73× better computing efficiency and 1.36× higher energy efficiency than existing approaches.

In recent years, the providers of public and private sector health care services have been faced with some radical changes in the society they serve, and more importantly, development in the way that traditional health care is delivered to information technology (I.T.) based communities. It is widely believed by health care professionals that the better health care results really come from the improved healthcare systems and more effective health care services' management. This paper focuses on using an agent-based software engineering approach and design models to the development of an appropriate agent-based healthcare software system is described in which software researchers collaborate with environment builders to enhance the levels of cooperation and support provided within an integrated agent-based community healthcare system

Open-set domain adaptation (OSDA) is crucial in intelligent fault diagnosis, enabling knowledge transfer from labeled source domains to unlabeled target domains while detecting unknown faults. Existing OSDA methods often rely on softmax scores or fixed class boundaries, leading to overconfident predictions and misclassifications. To address this, this paper proposes GACS-OSDA, which combines a Gaussian auxiliary classifier with separation loss for accurate fault classification and unknown fault detection. GACS-OSDA models each known class as a Gaussian distribution in feature space, using a Gaussian negative log-likelihood loss to align features with their class distributions while handling uncertainty. To improve class separability, a separation loss enforces the distance between class means, creating distinct distributions. For unknown fault detection, dynamic thresholding adapts to each class’s uncertainty, identifying samples that significantly deviate from known classes. Experiments on two fault diagnosis datasets show that GACS-OSDA surpasses other OSDA methods in classification accuracy and unknown fault detection.