Oikonomos-II+ is a hybrid, reinforcement-learning system for recommending optimal cloud-instance types for High-Performance Computing (HPC) and Artificial-Intelligence (AI) applications. Unlike existing approaches that require historical data or repeated job executions, Oikonomos-II+ learns online using user-submitted jobs. It combines a modified Neural-LinUCB algorithm with Gaussian-Process regression to model the relationship between job parameters, instance types, and execution time. This allows it to balance exploration and exploitation efficiently, even in the absence of prior data. We evaluated six configurations of Oikonomos-II+ on a diverse set of HPC and AI workloads, optimizing for cost and speed. Results show that the complete system converges to optimal resource choices, outperforming purely predictive or search-based approaches. By treating deployed applications as a black box and by eliminating the need for preexisting training data or auxiliary runs, Oikonomos-II+ provides a general-purpose, low-overhead solution for dynamic resource selection in heterogeneous cloud environments.

The growing complexity and diversity of neural networks in the fields of autonomous driving and intelligent robots have facilitated the research of many-core architectures, which can offer sufficient programming flexibility to simultaneously support multi-DNN parallel inference with different network structures and sizes compared to domain-specific architectures. However, due to the tight constraints of area and power consumption, many-core architectures typically use lightweight scalar cores without vector units and are almost unable to meet the high-performance computing needs of multi-DNN parallel inference. To solve the above problem, we design an area- and energy-efficient many-core architecture by integrating large amounts of lightweight processor cores with RV32IMA ISA. The architecture leverages the emerging SRAM-based computing-in-memory technology to implement vector instruction extensions by reusing memory cells in the data cache instead of conventional logic circuits. Thus, the data cache in each core can be reconfigured as the memory part and the computing part with the latter tightly coupled with the core pipeline, enabling parallel execution of the basic RISC-V instructions and the extended multi-cycle vector instructions. Furthermore, a corresponding execution framework is proposed to effectively map DNN models onto the many-core architecture by using intra-layer and inter-layer pipelining, which potentially supports multi-DNN parallel inference. Experimental results show that the proposed MAICC architecture obtains a 4.3 × throughput and 31.6 × energy efficiency over CPU (Intel i9-13900k). MAICC also achieves a 1.8 × energy efficiency over GPU (RTX 4090) with only 4MB on-chip memory and 28 mm2 area.

Nowadays, the market of 3D human posture tracking has extended to a broad range of application scenarios. As current mainstream solutions, vision-based posture tracking systems suffer from privacy leakage concerns and depend on lighting conditions. Towards more privacy-preserving and robust tracking manner, recent works have exploited commodity radio frequency signals to realize 3D human posture tracking. However, these studies cannot handle the case where multiple users are in the same space. In this paper, we present a mmWave-based multi-user 3D posture tracking system, m3Track, which leverages a single commercial off-the-shelf (COTS) mmWave radar to track multiple users' postures simultaneously as they move, walk, or sit. Based on the sensing signals from a mmWave radar in multi-user scenarios, m3Track first separates all the users on mmWave signals. Then, m3Track extracts shape and motion features of each user, and reconstructs 3D human posture for each user through a designed deep learning model. Furthermore. m3Track maps the reconstructed 3D postures of all users into 3D space, and tracks users' positions through a coordinate-corrected tracking method, realizing practical multi-user 3D posture tracking with a COTS mmWave radar. Experiments conducted in real-world multi-user scenarios validate the accuracy and robustness of m3Track on multi-user 3D posture tracking.

BACKGROUND: Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns. METHOD: We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection. RESULTS: The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively. CONCLUSION: In conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.