Advances In Hardware Research Articles

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

ASIP Performance Enhancement by Hazard Control through Scoreboard

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Multi-objective optimization algorithms for building performance assessment – A benchmark

The increasing development in the computational field that allowed software and hardware advances enables more refined building performance analysis. Simulation-based optimization (SBO) methods allow high standards to be achieved by combining parametric modeling, simulation, and optimization methods. However, SBO methods still need development, especially regarding the correct choice of the optimization algorithm based on the specific characteristics of each problem. This study proposes a multi-objective optimization algorithms benchmark by comparing seven multi-objective optimization algorithms: RBFMOpt, NSGA2, MHACO, NSPSO, MOEA/D, HypE, and SPEA2, across nine building-related problems, including thermal, energy, and daylight simulation. The problems varied from 5 to 18 discrete, continuous, and mixed parameters. The objective functions varied between two and three. We used the hypervolume indicator, IGD+, GD+, and EPS + to compare algorithms’ performance and assess the tendency to reduce computational costs. We also performed the Kruskal-Wallis non-parametric test to analyze the impact of multiple runs on the hypervolume indicator. The results showed that RBFMOpt and HypE perform best across all problems. However, RBFMOpt tends to reduce computational costs since the algorithm requires fewer simulations to obtain the best results.

Exploring AES Encryption Implementation Through Quantum Computing Techniques

A coming great revolution in technology is quantum computing, which opens new attacks on most of the developed cryptographic algorithms, including AES. These emerging quantum capabilities risk weakening cryptographic techniques, which safeguard a vast amount of data across the globe. This research uses Grover's algorithm to explore the vulnerabilities of the Advanced Encryption Standard to quantum attacks. By implementing quantum cryptographic algorithms and Quantum Error Correction on simulators and quantum hardware, the study evaluates the effectiveness of these techniques in mitigating noise and improving the reliability of quantum computations. The study shows that while AES is theoretically at risk due to Grover’s algorithm, which demonstrates a theoretical reduction in AES key search complexity, current hardware limitations and noise levels encountered in today’s quantum computers reduce the immediate threat and limit practical exploitation. The research also examines NTRU encryption, a quantum-resistant alternative, highlighting its robustness in quantum environments. The findings emphasize the need for further development in QEC and quantum-resistant cryptography to secure digital communications against future quantum threats. Future work will focus on advancing QEC techniques and refining quantum algorithms, addressing both hardware and theoretical advancements, including the potential use of high-capacity processors like Jiuzhang 3.0. These improvements will ensure the scalability of quantum-resistant systems to practical key sizes and usage scenarios.

Recovering MRI magnetic field strength properties using machine learning based on image compression quality scores

AbstractOver the years, significant hardware advancements have led to higher magnetic field MRI (Magnetic Resonance Imaging) acquisitions, providing improved diagnostic image quality at a higher cost. While higher image quality is desired, the investment required for high-field MRI imaging systems remains a significant consideration. Furthermore, historical patient records may still contain low-field magnetic strength MRI data. In this work, we present how image compression quality scores of MRI images could be used to recover their magnetic field strength properties. This work presents an interesting inverse problem scenario where output properties are used to recover an input property of an image. We implemented supervised machine learning models that utilize compressed image quality scores - based on Mean Squared Error (MSE), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF), and Earth Movers Distance (EM) metrics - as inputs. These models classify images into specific classes of magnetic field strength at which they were acquired (Low, Medium, and High) based solely on the quality scores of their compressed copies. The trained machine learning models (Support Vector Machine (SVM), Logistic Regression, K-nearest Neighbours (KNN), Decision Tree and Random Forest) achieved nearly 100% performance efficiency based on accuracy and F-1 score for all considered quality measures. This comprehensive evaluation offers insights into how compression techniques and quality factors impact image fidelity across various MRI magnetic field strengths. The findings of this study may prove valuable in recovering missing meta-tag information in historical image DICOM records and Context-Based Retrieval Systems (CBRS). The approach of using quality scores as features in training machine learning models, as demonstrated in this work, can be extended to other image groups where input parameters create statistically significant distinctions or are challenging to extract from images.

Fine Granularity Is Critical for Intelligent Neural Network Pruning.

Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) can remove more total network parameters before reaching a given degree of accuracy loss, while methods that preserve some or all of a network's structure (coarser granularity, such as pruning channels from a CNN) take better advantage of hardware and software optimized for dense matrix computations. We compare intelligent iterative pruning using several different criteria sampled from the literature against random pruning at initialization across multiple granularities on two different architectures and three image classification tasks. Our work is the first direct and comprehensive investigation of the relationship between granularity and the efficacy of intelligent pruning relative to a random-pruning baseline. We find that the accuracy advantage of intelligent over random pruning decreases dramatically as granularity becomes coarser, with minimal advantage for intelligent pruning at granularity coarse enough to fully preserve network structure. For instance, at pruning rates where random pruning leaves ResNet-20 at 85.0% test accuracy on CIFAR-10 after 30,000 training iterations, intelligent weight pruning with the best-in-context criterion leaves it at about 90.0% accuracy (on par with the unpruned network), kernel pruning leaves it at about 86.5%, and channel pruning leaves it at about 85.5%. Our results suggest that compared to coarse pruning, fine pruning combined with efficient implementation of the resulting networks is a more promising direction for easing the trade-off between high accuracy and low computational cost.

Intensity- and time-based strategies for micro/nano-sizing via single-particle ICP-mass spectrometry: A comparative assessment using Au and SiO2 as model particles

BackgroundSingle-particle ICP-mass spectrometry (SP-ICP-MS) is a powerful method for micro/nano-particle (MNP) sizing. Despite the outstanding evolution of the technique in the last decade, most studies still rely on traditional approaches based on (1) the use of integrated intensity as the analytical signal and (2) the calculation of the transport efficiency (TE). However, the increasing availability of MNP standards and advancements in hardware and software have unveiled new venues for MNP sizing, including TE-independent and time-based approaches. This work systematically examines these different methodologies to identify and summarize their strengths and weaknesses, thus helping to determine their preferred application areas. ResultsDifferent SP-ICP-MS methods for MNP sizing were assessed using AuNPs (20–70 nm) and SiO2MNPs (100–1000 nm). Among TE-dependent approaches, the particle frequency method was characterized by larger uncertainties than the particle size method. The results of the latter were dependent on the appropriate selection of the reference MNP, making the use of multiple reference MNPs recommended. TE-independent methods were based on external (linear and polynomial) calibrations and a relative approach. These methods exhibited the lowest uncertainties of all the strategies evaluated. External calibrations benefited from simpler calculations, but their application could be hindered by a lack of reference MNPs within the desired size range or by the need for interpolations outside the calibration range. Finally, transit time signals are directly proportional to the MNP size rather than its mass. The time-based method demonstrated adequate performance for sizing AuNPs but failed when sizing the largest SiO2MNPs (1000 nm). Significance and noveltyThis work provides further insights into the application of different SP-ICP-MS methodologies for MNP sizing. Both TE-independent approaches and the monitoring of the transit time as the analytical signal are underused strategies; in this context, a Python script was developed for accurate transit time measurement. After 20 years of development, a quantitative comparison of the different methodologies, including the most novel approaches, is deemed necessary for further growth on solid theoretical ground.

Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection.

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables.

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery.

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

AI-Enhanced Edge Device for Real-Time Snoring Detection

This paper presents the development of a cutting-edge, non-invasive edge device designed to monitor snoring and provide timely, moderate haptic feedback to users. Utilizing the Qualcomm Snapdragon 8cx Gen 3 processor, the device offers robust computing power and AI capabilities for real-time processing, making it a versatile tool for health monitoring applications. The system integrates a high-fidelity MEMS microphone array capable of capturing nuanced audio signals and a TDK piezoelectric haptic actuator, which delivers precise alerts through customized vibrations. The research explores the potential of this advanced hardware in detecting and managing obstructive sleep apnea (OSA), a condition often underdiagnosed due to a lack of patient awareness. By leveraging state-of-the-art digital signal processing and deep learning techniques, the device aims to enhance user awareness and intervention in sleep-related disorders, offering a promising new avenue for improving patient outcomes and quality of life.

Intrusion Detection System Application with Machine Learning

Information security holds paramount importance for organizations and users alike, safeguarding against unauthorized access to sensitive data. Daily usage of the internet amplifies the importance of security measures and the detection of malicious activities. Cyber-attacks, as these malicious activities are commonly known, are continually evolving with advancements in hardware, software, and complex network algorithms. Intrusion Detection Systems play a crucial role in shielding data and information from cyberattacks. The rapid progression in machine learning and deep learning, two popular methodologies in data mining, has found applications in various fields, including security. This study focuses on the use of machine learning and deep learning methods to design an intelligent intrusion detection system. For the development of this smart intrusion detection system, two well-established datasets, NSL-KDD and Kyoto 2006+, were employed. Machine learning methods were implemented utilizing the classification algorithms available in the WEKA data mining tool. The results obtained from these classification algorithms were compared with the deep learning model designed within the scope of the study. Consequently, a detailed analysis of machine learning and deep learning methods on the NSL-KDD and Kyoto 2006+ datasets for an intelligent intrusion detection system was conducted, and suggestions were proposed for further research endeavors.

DESIGN and be SMART: Eleven engineering challenges to achieve sustainable air transportation under safety assurance in the year 2050

The aviation industry faces various challenges in meeting long-term sustainability goals amidst surging demand for air travel and growing environmental concerns of the general public. The year 2050 is set as an ambitious goal for net zero emissions, a substantial reduction in carbon dioxide emissions per passenger kilometer flown, major improvements in aircraft energy efficiency, and a development towards autonomous, intelligent operations. This review explores the pivotal role of advancements in engineering for achieving sustainability in aviation. Through a comprehensive review of existing literature and case studies, our work highlights how innovations in all aspects of aircraft engineering coupled with operations-related technologies, offer promising solutions to mitigate environmental impact, enhance efficiency, and ensure long-term sustainability in aviation operations. To discuss the necessary advances, we promote the so-called ‘DESIGN and be SMART’ framework, consisting of eleven complementary engineering challenges towards reaching sustainability. To address the high safety levels reached in air transportation, our DESIGN and be SMART framework also addresses the safety assurance challenge that is overarching each of the eleven engineering challenges. We believe that through an orchestrated integration of hardware advancements with innovative software solutions, and novel safety assurance methods, the aviation industry can realize synergistic benefits that drive sustainable growth of air transportation. Our review contributes to such an orchestration by describing the status quo and research challenges ahead.

Quantum Computing and Cloud Technologies: The Next Frontier in Computing Power

Abstract: This article explores the convergence of quantum computing and cloud technologies, representing a pivotal advancement in computational power with far-reaching implications across various industries and scientific domains. It examines the current state of quantum computing, elucidating its fundamental principles and recent hardware advancements, while comparing its capabilities to classical computing systems. The integration of quantum computing with cloud platforms is analyzed, highlighting the emergence of cloud-based quantum services and their advantages in democratizing access to this cutting-edge technology. The article delves into potential applications of quantum cloud computing, including cryptography, material science, artificial intelligence, and financial modeling, showcasing its transformative potential. Critical challenges facing quantum cloud computing are addressed, such as error correction, scalability issues, and the development of quantumsafe encryption methods. Looking ahead, the article discusses prospects and research directions, including anticipated breakthroughs in quantum hardware, emerging quantum cloud architectures, and the potential impact on various industries. By synthesizing current research and industry developments, this comprehensive overview provides insights into the revolutionary potential of quantum cloud computing and its role in shaping the future of computational capabilities.

The Application of Deep Learning to Accurately Identify the Dimensions of Spinal Canal and Intervertebral Foramen as Evaluated by the IoU Index.

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Rotation-invariant image recognition using interconnected floating-gate phototransistor

Rotational invariance is fundamental for robust image recognition systems, ensuring accurate analysis irrespective of image orientation. However, existing systems predominantly reliant on software often encounter challenges such as increased computational demands and compromises between processing speed and accuracy. In this study, we propose leveraging the interconnected floating-gate (FG) structure as an effective hardware-level solution to achieve rotational invariance in image recognition. Our design features a reconfigurable two-dimensional material FG phototransistor array, where each processing unit integrates four sensory devices sharing a common FG. This configuration facilitates uniform distribution of stored charges across the interconnected FG layer, which is typically made of metal, enabling consistent application of a single weight matrix to images across varied rotational conditions. The photoactive material, tungsten diselenide (WSe2), possesses a distinctive bipolar property that facilitates both hole and electron tunneling into the FG layer. This property directly contributes to the efficiency of state transition within the setup and improves its overall adaptability. In this manner, our design achieves stable and predictable outputs in recognizing identical digital numbers regardless of their rotation, while also demonstrating variable performance essential for accurately distinguishing between different digital numbers. This dual capability guarantees both the adaptability and precision required for rotation-invariant image recognition, suggesting that our work may open up a promising venue for exploring advanced hardware designs, such as optimized interconnected FG architectures, tailored for enhancing recognition accuracy and efficiency in the field of intelligent visual systems.

Advances in molecular imaging for early detection of lung cancer

Lung cancer remains the second most commonly diagnosed cancer globally and the leading cause of cancer-related deaths, a trend consistent in the United States as of 2023. One of the key reasons for the high mortality rate of lung cancer is its poor prognosis, with 75% of patients diagnosed at middle and advanced stages. Early detection of subclinical lung cancer, metastases, and their fibrotic stroma is crucial for enabling timely treatment, reducing reoccurrence, and stratifying patients. Current diagnostic methods, such as lung biopsy for patients with small nodules, are highly invasive and technically challenging. The radiological gold standard, computed tomography (CT), is associated with ionizing radiation. However, positron emission tomography (PET) and magnetic resonance imaging (MRI) have emerged as promising methodologies for lung cancer diagnosis. PET tracers with a variety of targeting mechanisms are currently under development in human trials. With advancements in hardware and software over the past decades, radiation-free MRI has been clinically and preclinically validated as an alternative to CT. Moreover, novel-targeted MRI contrast agents have been tested in animal models and show strong translational potential. In this review, we summarize the state-of-the-art progress in molecular imaging for the early detection of lung cancer and its potential biomarkers.

Enhancing the temporal resolution of water levels from altimetry using D-InSAR: A case study of 10 Swedish Lakes

Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.

How does obsolescence risk influence consumer resistance to smartwatches?

PurposeDue to advances in both software and hardware, obsolescence risk refers to the fear that a product will soon become obsolete, which can be very high for technological products such as smartwatches or smartphones. Drawing on the perceived risk theory and innovation resistance, this study examines the effects of different obsolescence risks on consumer resistance to smartwatches.Design/methodology/approachA sequential explanatory approach using a mixed method was adopted in this study. In Study 1, we conducted semi-structured and in-depth face-to-face interviews with 16 individuals to identify the most important obsolescence risks affecting consumers’ resistance to smartwatches. This qualitative study develops a novel theoretical model based on interpretive results, including technological, economic, functional, and aesthetic obsolescence risks. In Study 2, we tested our theoretical model by applying partial least-squares structural equation modeling to a sample of 298 smartwatch users.FindingsThe results show that consumer resistance to smartwatches is affected by technological, economic, functional, and aesthetic obsolescence risks.Originality/valueAlthough most extant studies have focused on the factors influencing the adoption and use of consumer electronics, little is known about the role of obsolescence risk in consumers’ resistance to these products.

Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy

AbstractMolecular‐level understanding of photoinduced processes is critically important for breakthroughs in transformative technologies utilizing light, ranging from photomedicine to photoresponsive materials. Theory and simulation play a crucial role in this task. Despite great advances in hardware and computational methods, the theoretical description of photoinduced phenomena in the presence of complex environments and external photoexcitation conditions still poses formidable challenges for theoreticians and there are numerous formal and computational difficulties that must be overcome. The development of predictive, accurate, and at the same time, computationally efficient theoretical approaches to describe complex problems in photochemistry and photophysics is an active field of research in contemporary theoretical and computational chemistry. In this advanced review, we discuss modern computational advances and novel approaches that have been recently developed in excited‐electronic structure methods, and multiscale modeling, with a special emphasis on coupled electron‐nuclear dynamics and spectroscopy, from fully quantum to semi‐classical methodologies—including dissipative effects, the explicit light field interaction, femtosecond time‐resolved spectroscopy, and software infrastructure.This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Spectroscopy Software > Molecular Modeling

Smart parallel automated cryo-electron tomography.

In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context.