Efficient execution of tensor computations on different hardware, such as CPUs, GPUs, and spatial accelerators, poses substantial challenges due to divergent memory hierarchies, compute models, and architectural constraints. However, existing auto-tuning approaches typically fail to integrate hardware-aware formulations, leading to inefficient search processes and suboptimal utilization of hardware resources. To address these challenges, we introduce HAOT, a novel hardware-aware tensor computation optimization framework that combines a learning-based scheduling policy with a hardware analytical model. Unlike traditional methods, HAOT not only adapts the learning-based scheduling strategy but also dynamically constrains the search space based on the availability of hardware resources, ensuring both the feasibility and optimal use of resources. Experiments across a range of hardware platforms show that HAOT achieves up to 1.4× average speedup on individual tensor operations and a 1.8× improvement in end-to-end deep learning workloads, outperforming state-of-the-art baselines. Ablation studies demonstrate the complementary roles of the Transformer-based policy model and the hardware analysis model, each contributing to generating high-quality schedule decisions. Furthermore, HAOT exhibits superior sample efficiency, requiring significantly fewer search trials to converge to the optimal schedule.

Spectral videos (SVs) capture spatio-temporal-spectral information from dynamic scenes, but their acquisition traditionally requires expensive and complex systems, motivating the development of compressive spectral video sensing (CSVS). It typically employs the coded aperture snapshot spectral imager (CASSI) to acquire compressed measurements, from which SVs are reconstructed via model-driven or learning-based algorithms. However, two major limitations remain in current CASSI-based reconstruction methods: 1) conventional model-driven algorithms rely on iterative optimization, which limits their representational capacity in complex scenes and results in slow reconstruction; 2) existing deep learning-based approaches overlook the joint modeling of spatial, temporal, and spectral correlations, failing to fully exploit the multi-dimensional dependencies. Hence, we propose a principled compressive spectral video sensing unfolding framework (CSVSUF) in a CASSI system for spectral video reconstruction. Moreover, we develop a novel spatio-temporal-spectral prior-learning Transformer (STS-PLT) to capture the multi-dimensional correlations within each unfolding stage. By treating STS-PLT as a Gaussian denoiser for the prior term in CSVSUF, we establish a deep unfolding-based method for CSVS. Extensive experiments demonstrate that our method consistently outperforms existing approaches in both reconstruction accuracy and visual quality, validating the benefit of combining physics-guided modeling with deep prior learning in CSVS. Code is available at https://github.com/zli1024/CSVSUF.

The rich spatial and spectral information in hyperspectral images (HSIs) makes spectral-spatial relationships essential for HSI classification (HSIC). Recent advancements indicate convolutional neural networks (CNNs) excel in HSIC but often struggle with precise spectral feature extraction. Moreover, the abundance of spectral information presents challenges in efficient feature representation and minimizing cross-domain interference. To address these limitations, we propose an efficient sequential spectral-spatial feature convolution network (S3FCN), employing successive subnetworks for spectral and spatial feature extraction with depthwise separable convolution. This approach balances the preservation of deep spectral and spatial features while significantly reducing network parameters, enhancing both performance and computational efficiency. We also introduce a sequential spectral-spatial attention module (S3AM) to integrate cross-domain correlations. This module utilizes spectral features from the preceding subnetwork and multilevel residual layers for in-depth exploration of spatial features, enabling deep integration for improved classification performance. The proposed architecture’s effectiveness is verified on five benchmark HSI datasets, including Pavia University, Salinas Valley, Kennedy Space Center, Indian Pines, and Houston 2013. Experimental results demonstrate that the sequential spectral-spatial connection in the feature extraction and attention mechanism integrated with depthwise separable convolution collectively surpasses current state-of-the-art (SOTA) techniques in classification accuracy with overall accuracies of 98.28%, 97.63%, 99.31%, 96.72%, and 95.38% across different datasets, while limiting the computation overhead, ensuring balanced network efficiency.

Alzheimer’s disease (AD) is a degenerative neurological disease that occurs in the elderly with typical symptoms of decline in cognition, manifested by eye movement behaviors. The key to AD treatment requires early detection of cognitive impairment, which relies on frequent medical screening. This article proposes an Internet of Things (IoT) architecture constructed with eye-tracker (ET) nodes and cloud-based diagnosis enabled by machine learning (ML), which can provide convenient screening of oculomotor abnormalities and automatic identification of early-stage AD. The bespoke ET nodes collect 3-D oculomotor responses from diverse stereo video stimulation trials and transmit data into a dedicated multimodal ML (MMML) algorithm in the cloud. The algorithm incorporates multimodal features extracted from diverse oculomotor types to enhance the classification accuracy and optimized data dimension reduction in feature fusion to improve the classifier’s performance. From evaluation, the proposed method can distinguish AD patients from the control group with 86% accuracy (ACC), 78% true positive rate (TPR), and 90% positive predictive value (PPV). The results confirm the effectiveness of our MMML algorithm in AD diagnosis with the fusion of multimodal oculomotor features and prove the feasibility of our IoT-powered eye-tracking solution for AD screening.