Hardware Innovations Research Articles

Digital detector PET/CT increases Centiloid measures of amyloid in Alzheimer's disease: A head-to-head comparison of cameras.

The introduction of therapeutics for Alzheimer's disease has led to increased interest in precisely quantifying amyloid-β (Aβ) burden for diagnosis, treatment monitoring, and further clinical research. Recent positron emission tomography (PET) hardware innovations including digital detectors have led to superior resolution and sensitivity, improving quantitative accuracy. However, the effect of PET scanner on Centiloid remains relatively unexplored and is assumed to be minimized by harmonizing PET resolutions. To quantify the differences in Centiloid between scanners in a paired cohort. 36 participants from the Australian Imaging, Biomarker and Lifestyle study (AIBL) cohort were scanned within a year on two scanners. Each participant underwent 18F-NAV4694 imaging on two of the three scanners investigated, the Siemens Vision, the Siemens mCT and the Philips Gemini. We compared Aβ Centiloid quantification between scanners and assessed the effectiveness of post-reconstruction PET resolution harmonization. We further compared the scanner differences in target sub-regions and with different reference regions to assess spatial variability. Centiloid from the Vision camera was found to be significantly higher compared to the Gemini and mCT; the difference was greater at high-Centiloid levels. Post-reconstruction resolution harmonization only accounted for and corrected ∼20% of the Centiloid (CL) difference between scanners. We further demonstrated that residual differences have effects that vary spatially between different subregions of the Centiloid mask. We have demonstrated that the type of PET scanner that a participant is scanned on affects Centiloid quantification, even when scanner resolution is harmonized. We conclude by highlighting the need for further investigation into harmonization techniques that consider scanner differences.

From Analog to Digital: Role of Intraoral Scanners in Dentistry - A Review Article

Intraoral scanners (IOS) have revolutionized the field of dentistry, offering a cutting-edge alternative to traditional impression methods. These innovative devices enable dentists to capture highly accurate digital impressions of a patient's teeth and surrounding tissues, streamlining the treatment process and enhancing patient care. Recent years have witnessed notable IOS technology advancements, characterized by hardware innovations and software breakthroughs. These developments improved intraoral scanning efficiency and accuracy in computer-aided design and computer-aided manufacturing (CAD-CAM), which remains IOS’s most common use case. Keywords: Intraoral scanners (IOS), computer-aided design and computer-aided manufacturing (CAD-CAM), Dentistry.

Assessing Design Space for the Device-Circuit Codesign of Nonvolatile Memory-Based Compute-in-Memory Accelerators.

Unprecedented penetration of artificial intelligence (AI) algorithms has brought about rapid innovations in electronic hardware, including new memory devices. Nonvolatile memory (NVM) devices offer one such attractive alternative with ∼2× density and data retention after powering off. Compute-in-memory (CIM) architectures further improve energy efficiency by fusing the computation operations with AI model storage. Electronic characteristics of NVM devices, like resistance in the two resistance states, directly affect the circuit designers' decisions and result in the varying performance of NVM-CIM chips. In this mini review, we assess the bounds on device resistances for accuracy and circuit performance to suggest recommendations to device engineers for frictionless device-circuit-system interactions. Furthermore, we review challenges in reliably programming NVM devices, followed by benchmarking recent NVM-CIM chips. Our literature review and analytical modeling reveal that a high resistance ratio and low variability are favored, and the resistance in a low resistance state is bound by accuracy and circuit performance constraints.

(Keynote) Atomic Layer Processing in Semiconductor Research and Development

As semiconductor process technology has evolved from node to more advanced node, continued atomic layer processing innovations in materials and hardware have improved film quality, device performance and overall cost. In this talk, Intel will cover atomic layer processes and where they have been critical to the high-volume manufacturing of transformational new semiconductor products.As the semiconductor industry moves into the gate all around RibbonFET era, atomic layer control is required across more layers with ever increasing geometric complexity. New device architectures institute hard limits on film thickness which make atomic layer processes ideal. In fact, for some applications, the low growth per cycle or etch per cycle of atomic layer processes are required to achieve the fine control needed for optimal device performance. Other traditionally used deposition techniques will not be able to scale to these new devices; atomic layer processes will need to step in by delivering high quality dielectrics, metals and semiconductor materials into smaller, more complex device shapes. These materials are often multi-component and require precise compositional control, including that of dopants. Next generation devices are increasing multi-colored meaning that an increasing variety of materials are found in close proximity to each other. The ability to selectively place materials only where needed, through intrinsically selective processes, inhibitor chemistries and/or subsequent selective etch techniques can enable new structures and reduce patterning costs. Molecular layer deposition of EUV photoresists is also being explored to introduce high absorptivity elements for next generation lithographic patterning. These challenges, and the resultant opportunities, will be magnified as coplanar CMOS nanoribbon devices are replaced with stacked nanoribbon architectures.Finally, we will cover the largest opportunities for future devices, covering needs for atomic layer processes for the integration of two-dimensional materials, e.g., graphene or transition metal dichalcogenides, and the emerging field of epitaxial complex oxides for ultra-low energy devices which necessitate the development of chemical vapor phase etches of non-traditional materials for the semiconductor industry.

Quantum Computing in Medicine.

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum-classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.

The Future of Artificial Intelligence: Trends and Predictions

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.

Development and Initial Testing of an Artificial Intelligence-Based Virtual Reality Companion for People Living with Dementia in Long-Term Care.

Background/Objectives: Feelings of loneliness are common in people living with dementia (PLWD) in long-term care (LTC). The goals of this study were to describe the development of a novel virtual companion for PLWD living in LTC and assess its feasibility and acceptability. Methods: The computer-generated virtual companion, presented using a head-mounted virtual reality display, was developed in two stages. In Stage 1, the virtual companion asked questions designed to encourage conversation and reminiscence. In Stage 2, more powerful artificial intelligence tools allowed the virtual companion to engage users in nuanced discussions on any topic. PLWD in LTC tested the application at each stage to assess feasibility and acceptability. Results: Ten PLWD living in LTC participated in Stage 1 (4 men and 6 women; average 82 years old) and Stage 2 (2 men and 8 women; average 87 years old). Session lengths ranged from 0:00 to 5:30 min in Stage 1 and 0:00 to 53:50 min in Stage 2. Speech recognition issues and a limited repertoire of questions limited acceptance in Stage 1. Enhanced conversational ability in Stage 2 led to intimate and meaningful conversations with many participants. Many users found the head-mounted display heavy. There were no complaints of simulator sickness. The virtual companion was best suited to PLWD who could engage in reciprocal conversation. After Stage 2, response latency was identified as an opportunity for improvement in future versions. Conclusions: Virtual reality and artificial intelligence can be used to create a virtual companion that is acceptable and enjoyable to some PLWD living in LTC. Ongoing innovations in hardware and software will allow future iterations to provide more natural conversational interaction and an enhanced social experience.

Confidential Computing in the Cloud: An Overview

Major financial institutions like Goldman Sachs and JP Morgan have employed these hardware-based trusted execution environments (TEEs) and reported a 50% reduction in data breaches and a 40% increase in customer trust. Daily, these companies do billions of transactions in the cloud, leveraging confidentiality computing to ensure the privacy and integrity of their sensitive data. Over the years, confidential computing has evolved significantly, and the emergence of technology to safeguard sensitive information from malicious insiders and external threats now encompasses advanced and complex cryptographic techniques and hardware innovations, offering robust security assurances for cloud-based operations. In this paper, we will talk about foundational technologies and implementation strategies for the core of confidential computing. We will explore benefits, including performance trade-offs and integration complexities. Furthermore, the paper will highlight real-world applications and use cases, showcasing how industries such as finance, healthcare, and government leverage confidential computing to enhance data security and complicate cloud environments.

Linking fast and slow: The case for generative models.

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Development of Diagnosis System for Rolling Bearings Faults on Real Time Based on FPGA

The real-time monitoring of events in an industrial plant is an advanced technique that presents the real conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes various rotating machinery condition monitoring techniques of the machine to determine the conditions of failure. To increase the operational reliability and to reduce preventive maintenance, it is necessary an efficient tool for analysis and process monitoring, in real time, enabling the detection of incipient faults for rolling bearings. Over the past few years there has been a major technological developments related to digital system, including innovations in both hardware and software. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades and expansions of the designed system. This paper presents a study of new design tools for embedded digital systems based on open hardware architecture with reconfigurable logic. Will be discussed a case study in the area of fault detection of rolling bearings, as well as its implementation and testing.

Titanium Alloy Implants with Lattice Structures for Mandibular Reconstruction.

In recent years, the field of mandibular reconstruction has made great strides in terms of hardware innovations and their clinical applications. There has been considerable interest in using computer-aided design, finite element modelling, and additive manufacturing techniques to build patient-specific surgical implants. Moreover, lattice implants can mimic mandibular bone's mechanical and structural properties. This article reviews current approaches for mandibular reconstruction, their applications, and their drawbacks. Then, we discuss the potential of mandibular devices with lattice structures, their development and applications, and the challenges for their use in clinical settings.

Unleashing the Potential of Sparse DNNs Through Synergistic Hardware-Sparsity Co-Design

Sparsity, a widely recognized path to curbing the computational needs of deep neural networks (DNNs), still suffers a number of roadblocks in practice, despite a decade of intensive research on sparse neural networks. The extant structured sparsity patterns often fail to attain significant model compression, while the hardware challenges posed by unstructured sparsity are yet to be fully overcome. As algorithmic and hardware innovations individually deliver limited benefits, a synergistic approach is necessary to unleash the potential of sparse DNNs. This work proposes a tightly integrated design methodology for the sparsity patterns and associated hardware platforms to reach the highest model compression goals while simultaneously facilitating efficient hardware processing. We demonstrate that novel complementary sparsity patterns can offer utmost expressiveness levels with inherent hardware exploitable regularity. Our novel dynamic training method converts the expressiveness of such sparsity configurations into highly accurate and compact sparse neural networks. Complementary sparsity is represented in a dense format, and when synergistically coupled with minimal yet strategic hardware modifications, can be processed in close concordance with the conventional dataflow of the dense matrix operations. We thus demonstrate that there is ample room for innovation beyond conventional techniques and immense practical potential for sparse neural networks through the synergistic design of sparsity patterns and hardware architectures.

A Survey on Virtual Production and the Future of Compositing Technologies

In recent years, virtual production has seen its largest growth in popularity in the film industry. The demand for faster and cheaper productions, the continuous innovations in hardware and real-time rendering, the globalization of virtual and augmented reality and the most recent need for remote collaboration are all driving factors for its consolidation in the film production lifecycle. Although the benefits of a virtual production approach have an impact on many departments, the compositing and visual effects department is one of the most affected. This paper explores the evolution of compositing technologies and describes why virtual production is a natural fit. We also explore how compositing workflows today are being influenced by virtual production and discuss the prospects of compositing techniques.

Editorial: Innovations in MR hardware from ultra-low to ultra-high field.

Editorial: Innovations in MR hardware from ultra-low to ultra-high field.

Chasing Carbon: The Elusive Environmental Footprint of Computing

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This article brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing, thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We, therefore, outline future directions for minimizing the environmental impact of computing systems.

High-throughput whole-slide scanning to enable large-scale data repository building.

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost‐effective manner. Technical innovation in whole‐slide imaging has enabled high‐throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher‐quality imaging data using fewer personnel. Here we review several practical considerations for deploying high‐throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high‐throughput scanning realizable to laboratories with limited resources. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series.

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Seeking High IMP Reliability in Maintenance of the 1970s ARPAnet

During the first years of ARPAnet operations, computers were not highly reliable, but the network was built from standard computers and was expected to function as a utility with high reliability. We managed to achieve the desired reliability, as perceived by ARPAnet users, by making innovations in hardware, maintenance procedures, software, and network operations. This article draws heavily on the personal experiences of the authors, many of which have not been previously reported in the literature. Our focus is on the 1969–1975 time period when ARPAnet was the sole responsibility of the Advanced Research Projects Agency (ARPA). A 2018 paper (Fidler et al., 2018) discusses ARPAnet maintenance after 1975. The preparation of this article was spearheaded by David Walden. David recruited the other 2 authors, prepared the outline of the paper he envisioned, and drafted some sections. Sadly David's deteriorating health prevented him from contributing his usual amount of energy and knowledge, but this article would not have been written without him.

Recognition of the finger vascular system using multi‐wavelength imaging

There has recently been intensive development of methods for identification and personal verification using the human finger vascular system (FVS). The primary focus of these efforts has been the increasingly sophisticated methods of image processing, and frequently employing machine learning. In this article, we present a new concept of imaging in which the finger vasculature is illuminated using different wavelengths of light, generating multiple FVS images. We hypothesised that the analysis of these image sets, instead of individual images, could increase the effectiveness of identification. Analyses of data from over 100 volunteers, using five different deterministic methods for feature extraction, consistently demonstrated improved identification efficiency with the addition of data obtained from another wavelength. The best results were seen for combinations of diodes between 800 and 900 nm. Finger vascular system observations outside this range were of marginal utility. The knowledge gained from this experiment can be utilised by designers of biometric recognition devices leveraging FVS technology. Our results confirm that developments in this field are not restricted to image processing algorithms, and that hardware innovations remain relevant.

Effective Scaling of Blockchain Beyond Consensus Innovations and Moore’s Law: Challenges and Opportunities

As an emerging technology, blockchain has achieved great success in numerous application scenarios, from intelligent healthcare to smart cities. However, a long-standing bottleneck hindering its further development is the massive resource consumption attributed to the distributed storage and consensus mechanisms. This makes blockchain suffer from insufficient performance and poor scalability. In this article, we evaluate numerous representative blockchain proposals in the past decade and demonstrate that the potential of the widely adopted consensus-based scaling is seriously limited, especially in the current era when Moore’s law-based hardware evolution is about to end. We achieve this by developing an open-source benchmarking tool, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</monospace> , with which we further investigate the resource-related factors that hinder the current scaling attempts. Furthermore, we discuss various topology and hardware innovations which could help to scale up blockchain. Generally speaking, this article provides blockchain researchers many valuable insights because it explores the next-generation scaling strategies by conducting a large-scale benchmarking.