Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising solution for energy-efficient acceleration across multiple application domains. Yet, CGRAs face significant scalability challenges that hinder their widespread adoption, stemming from three main concerns: (1) Mapping Scalability — existing mapping algorithms struggle to find feasible and optimal solutions as the design complexity grows; (2) Architectural Limitations — rigid mapping granularity and memory access restrict flexibility and performance; and (3) Dynamic Multi-Kernel Support — dynamic and simultaneous execution of multiple kernels are not thoroughly explored, limiting the applicability of CGRAs in complex multi-kernel scenarios.

Automated segmentation of coronary angiography images is highly significant for computer-aided diagnosis of coronary heart disease. However, existing segmentation methods suffer from the problem of poor segmentation results caused by insufficient extraction and fusion of the features of the complex topological structure of blood vessels. In view of this, this paper proposes an automated segmentation method for coronary angiography images based on SFAG-DeepLabv3+. This method utilizes the Swin Transformer network to screen coronary angiography images and proposes a Filtering Smoothing Equalization (FSE) image enhancement method to improve the quality of angiography images. Furthermore, this paper proposes an improved automatic segmentation network for coronary arteries based on the DeepLabv3+. In the encoder section, an Adaptive hybrid Dilated convolution and double Pooling (ADP) module is proposed to enhance the ability to extract topological features of coronary blood vessels. Between the encoder and decoder, a Gaussian Context Spatial Fusion (GCSF) module is proposed to reduce information loss during the compression and decompression of information from the encoder to the decoder. In the decoder section, bicubic interpolation upsampling is employed to improve the continuity of the segmented blood vessel topology. To validate the effectiveness of the proposed method, experiments were conducted using both the ARCADE public dataset and a self-constructed CSH dataset. Experimental results demonstrate that the method proposed in this paper can perform effective feature extraction, fusion and correction on coronary angiography images, achieving average Dice coefficients of 0.9249 on the CSH dataset and 0.9156 on the ARCADE dataset.

Fully Homomorphic Encryption (FHE) is an attractive privacy-preserving technique that allows computation directly on encrypted data without decryption. However, it incurs significant performance and memory costs due to intensive computations. In this work, we investigate the execution of FHE-enabled machine learning (ML) applications. We show that the runtime hardware reconfigurability of the underlying execution units of homomorphic operations is highly desirable for efficient hardware resource utilization during FHE-ML execution, due to the changing FHE encryption variants across different ML stages (e.g., the multiplicative level of the ciphertext) and corresponding optimal execution unit design. Based on the observation, we propose FHE-CGRA, a coarse-grained re-configurable architecture (CGRA) acceleration framework with an MLIR-based compiler toolchain for end-to-end homomorphic applications. The experiment shows that FHE-CGRA achieves up-to 8.15× speedup against a conventional CGRA baseline for accelerating the inference of FHE-encrypted convolution neural network (FHE-CNN) models, and up-to 16.48× power efficiency w.r.t. the state-of-the-art FPGA-based FHE-CNN accelerator design.

Coarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. However, the relationships of voltage and frequency with the utilization of CGRA resources and the dynamic management of them are not well explored, leading to inefficient designs. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non-performance-constraining kernels. This paper proposes ICED - an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICED proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICED is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICED improves average utilization by$\mathbf{2}.\mathbf{3}\times$and energy-efficiency by$\mathbf{1}.\mathbf{32}\times$over a conventional CGRA. With streaming applications, ICED can achieve up to$\mathbf{1}.\mathbf{26}\times$energy-efficiency compared with a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.

Hand-held augmented reality (AR) offers accessible, interactive rehabilitation options for patients with upper limb motor deficits. Incorporating hand-involved interactions (e.g., tangible and mid-air interactions) into hand-held AR provides patients with intuitive manners to perform rehabilitation exercises mimicking real-world activities. Previous work has shown the importance of user experience and motor performance in rehabilitation systems, but little was known in the literature regarding the impact of hand-involved interactions in hand-held AR on user experience and motor performance in rehabilitation exercises. Hence, this study aims to evaluate user experience and motor performance when using three types of hand-involved interactions in hand-held AR rehabilitation: (1) tangible cube (i.e., a space-multiplexed tangible interaction with a physical cube acting as a real proxy to manipulate a virtual object in the same form); (2) tangible controller (i.e., a time-multiplexed tangible interaction with a physical controller applied to manipulate a virtual object); and (3) hand motion (i.e., a form of mid-air interaction to move a virtual object with hands). Based on the findings from self-report, electroencephalography (EEG), and performance measures, this study reveals the advantages of the tangible cube over the tangible controller, both superior to the hand motion in hand-held AR rehabilitation regarding user experience and motor performance. This study offers new understanding of the advantages and disadvantages of various interaction techniques in hand-held AR rehabilitation, emphasizing crucial design considerations for these systems, with a focus on user experience and motor performance in upper limb rehabilitation.