Design and Testing Strategies for Modular 3-D-Multiprocessor Systems Using Die-Level Through Silicon Via Technology

An innovative modular 3-D stacked multi-processor architecture is presented. The platform is composed of completely identical stacked dies connected together by through-silicon-vias (TSVs). Each die features four 32-bit embedded processors and associated memory modules, interconnected by a 3-D network-on-chip (NoC), which can route packets in the vertical direction. Superimposing identical planar dies minimizes design effort and manufacturing costs, ensuring at the same time high flexibility and reconfigurability. A single die can be used either as a fully testable standalone chip multi-processor (CMP), or integrated in a 3-D stack, increasing the overall core count and consequently the system performance. To demonstrate the feasibility of this architecture, fully functional samples have been fabricated using a conventional UMC 90 nm complementary metal–oxide–semiconductor process and stacked using an in-house, via-last Cu-TSV process. Initial results show that the proposed 3-D-CMP is capable of operating at a target frequency of 400 MHz, supporting a vertical data bandwidth of 3.2 Gb/s

Decoding the impact of autoinflammatory/autoimmune diseases on inner ear harmony and hearing loss

Autoimmune and autoinflammatory diseases affecting the inner ear can cause symptoms such as hearing loss, imbalance, vertigo, and tinnitus, presenting demanding and often underdiagnosed conditions. Diagnostic challenges arise due to their diverse manifestations, potential long-term consequences, and the absence of specific serological markers, necessitating a multidisciplinary approach combining clinical evaluation, audiological assessments, and imaging techniques. Various autoimmune disorders, including systemic lupus erythematosus, rheumatoid arthritis, and Sjogren’s syndrome, have been implicated in immune-mediated damage to auditory structures, resulting in inner ear dysfunction. Inflammatory processes in autoinflammatory diseases like Cogan’s syndrome and relapsing polychondritis can also affect the inner ear. While the exact mechanisms of inner ear involvement in these conditions are still being studied, immune-mediated inflammation, damage to auditory structures, and vascular involvement play significant roles in auditory impairments. Treatment strategies primarily focus on immunomodulation and inflammation control using corticosteroids, immunosuppressants, and targeted biologic agents to ameliorate symptoms and preserve hearing function. Hearing aids and cochlear implants may be also considered for severe hearing loss. Individualized approaches are necessary due to patient response heterogeneity. This review provides a concise overview of key autoimmune and autoinflammatory diseases impacting the inner ear, highlighting clinical manifestations, diagnostics, pathophysiology, and treatment options. Early recognition and appropriate management are crucial for optimizing patient outcomes. Further research is needed to understand underlying mechanisms and identify novel therapeutic targets. Collaboration between otolaryngologists, rheumatologists, and immunologists is crucial for improving the quality of life in these complex conditions

Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review

Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords ‘otorhinolaryngology’, ‘imaging’, ‘computer vision’, ‘artificial intelligence’, and ‘deep learning’, and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology