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.

Effective mapping of dataflow graphs onto Coarse-Grained Reconfigurable Arrays necessitates compiler-architecture co-design, yet existing approaches frequently assume single-cycle operations despite real-world applications often involving multi-cycle operations that constrain achievable clock frequencies. To address this, we propose FLAME, a novel framework supporting three execution strategies (exclusive, distributed, inclusive) specifically designed for multi-cycle operations, with co-designed compiler and hardware support. Our evaluations demonstrate that FLAME not only surpasses prior methods in performance and but also enables flexible exploration of these operations. The framework achieves average speedups of 2.21× over baseline CGRA and 1.49× over prior state-of-the-art framework while highlighting the distinct characteristics of each strategy.

Many combinatorial optimization problems map naturally to Ising Hamiltonians, $H({\text{s}}) = - \sum\nolimits_{i,j} {{J_{ij}}} {s_i}{s_j} - \sum\nolimits_i {{h_i}} {s_i}$ , and CMOS ring-oscillator Ising machines solve them in microseconds at milliwatts [1] , [2] . Their key limitation is capacity : the number of spins one solver core can process in a single solve. Because each hardware spin represents one binary Ising variable, capacity directly sets the largest problem solvable in one shot. Our 28 nm five-core COBI chip solves a 45-spin all-to-all subproblem per core in 77.5 µ s, so larger instances require iterative decomposition. This shifts the bottleneck from analog solving to digital orchestration: a CPU-based decomposer needs ∼321 µ s/iter over PCIe, 4× the core solve time, leaving the solver idle 84.9% of the time. We instead co-locate an FPGA decomposer with the chip and derive sizing laws for the required parallelism, achieving 1.93× geomean speedup and > 40× energy reduction vs. an optimized C++ baseline.

Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory1, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.

The field of novel view synthesis from images has seen rapid advancements with the introduction of Neural Radiance Fields (NeRF) and more recently with 3D Gaussian Splatting. Gaussian Splatting became widely adopted due to its efficiency and ability to render novel views accurately. While Gaussian Splatting performs well when a sufficient amount of training images are available, its unstructured explicit representation tends to overfit in scenarios with sparse input images, resulting in poor rendering performance. To address this, we present a 3D Gaussian-based novel view synthesis method using sparse input images that can accurately render the scene from the viewpoints not covered by the training images. We propose a multi-stage training scheme with matching-based consistency constraints imposed on the novel views without relying on pre-trained depth estimation or diffusion models. This is achieved by using the matches of the available training images to supervise the generation of the novel views sampled between the training frames with color, geometry, and semantic losses. In addition, we introduce a locality preserving regularization for 3D Gaussians which removes rendering artifacts by preserving the local color structure of the scene. Evaluation on synthetic and real-world datasets demonstrates competitive or superior performance of our method in few-shot novel view synthesis compared to existing state-of-the-art methods.

In addition to color and textural information, geometry provides important cues for 3D scene reconstruction. However, current reconstruction methods only include geometry at the feature level thus not fully exploiting the geometric information.In contrast, this paper proposes a novel geometry integration mechanism for 3D scene reconstruction. Our approach incorporates 3D geometry at three levels, i.e. feature learning, feature fusion, and network supervision. First, geometry-guided feature learning encodes geometric priors to contain view-dependent information. Second, a geometry-guided adaptive feature fusion is introduced which utilizes the geometric priors as a guidance to adaptively generate weights for multiple views. Third, at the supervision level, taking the consistency between 2D and 3D normals into account, a consistent 3D normal loss is designed to add local constraints.Large-scale experiments are conducted on the ScanNet dataset, showing that volumetric methods with our geometry integration mechanism outperform state-of-the-art methods quantitatively as well as qualitatively. Volumetric methods with ours also show good generalization on the 7-Scenes and TUM RGB-D datasets.

Compared to 2D perspective images, panoramic images capture a larger field-of-view (FOV). Depth estimation from panoramas is an important task for 3D scene understanding and has made significant progress with the development of CNNs. However, existing CNN-based methods still suffer from the Equirectangular Projection (ERP) problem to deal with panoramic distortions (e.g. same receptive fields near the equator and the two poles) and have difficulty generating accurate depth boundaries. In contrast to existing CNN-based methods, in this paper, a novel Transformer-based method is proposed which is able to cope with panoramic distortions and to generate accurate depth boundaries. A Distortion-aware Transformer is designed using a yaw-invariant cycle shift and a distortion-guided partitioning. The aim is to alleviate the distortion effect by enlarging the receptive fields in both horizontal and vertical directions. Then, a Gradient Transformer is proposed to enhance the features around the boundaries. Gradient information is adopted as a boundary prior. Large-scale experimental results show an improvement compared to state-of-the-art methods. Our method also shows strong generalization capabilities. Finally, our method is extended to panorama semantic segmentation.