Hardware Techniques Research Articles

Transferable learning on analog hardware.

While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day training methods for programmable photonic interferometer circuits, a leading analog NN platform, do not produce networks that perform well in the presence of static hardware errors. Moreover, existing hardware error correction techniques either require individual retraining of every analog NN (which is impractical in an edge setting with millions of devices), place stringent demands on component quality, or introduce hardware overhead. We solve all three problems by introducing one-time error-aware training techniques that produce robust NNs that match the performance of ideal hardware and can be exactly transferred to arbitrary highly faulty photonic NNs with hardware errors up to five times larger than present-day fabrication tolerances.

A dataset for the visually impaired walk on the road

As a special group, visually impaired people (VIP) find it difficult to access and use visual information in the same way as sighted individuals. In recent years, benefiting from the development of computer hardware and deep learning techniques, significant progress have been made in assisting VIP with visual perception. However, most existing datasets are annotated in single scenario and lack of sufficient annotations for diversity obstacles to meet the realistic needs of VIP. To address this issue, we propose a new dataset called Walk On The Road (WOTR), which has nearly 190 K objects, with approximately 13.6 objects per image. Specially, WOTR contains 15 categories of common obstacles and 5 categories of road judging objects, including multiple scenario of walking on sidewalks, tactile pavings, crossings, and other locations. Additionally, we offer a series of baselines by training several advanced object detectors on WOTR. Furthermore, we propose a simple but effective PC-YOLO to obtain excellent detection results on WOTR and PASCAL VOC datasets. The WOTR dataset is available at https://github.com/kxzr/WOTR

MIMO DFRC Signal Design in Signal-Dependent Clutter

This paper deals with the Dual-Function Radar and Communication (DFRC) signal design for a Multiple-Input–Multiple-Output (MIMO) system, considering the presence of signal-dependent clutter. A modulation methodology called Spectral Position Index and Amplitude (SPIA) modulation is proposed, which involves selecting passband and stopband positions and applying amplitude modulation. Signal to Interference plus Noise Ratio (SINR) is maximized to enhance radar detectability. Meanwhile, variable modulus and communication modulation constraints are enforced to ensure compatibility with the current hardware techniques and communication demand, respectively. In addition, the mainlobe width and sidelobe level constraints used to concentrate energy in a specific area of space are enforced. To tackle the resulting nonconvex and NP-hard optimization problem, an Iterative Block Enhancement (IBE) framework that alternately updates each signal in each emitting antenna is exploited to monotonically increase SINR. Each block involves the Dinkelbach’s Iterative Procedure (DIP), Sequential Convex Approximation (SCA) and Alternating Direction Method of Multipliers (ADMM) to obtain a single signal. The computational complexity and convergence of the algorithm are analyzed. Finally, the numerical results highlight the effectiveness of the proposed dual-function scheme in sidelobe signal-dependent clutter.

Improved Data Encryption Standard Algorithm using Zigzag Scan for Secured Data Transmission

The cryptosystem is a combination of cryptographic algorithms used to provide security services for the information. One of them is the data encryption standard also known as DES which is a symmetric-key block cipher released by national bureau of standard (NBS). DES is a block cipher and perform encryption of each block of size 64 bits. Encryption of the data by using an algorithm which translates the original data into an unreadable format which is not easy for the intruder to attack. The DES is secure than the other cryptosystems, because the time required for processing cryptanalysis has minimized and because of the development in the hardware technique, the traditional DES may be unsafe by different kinds of attacks by the different cryptanalysis. This paper implements a new design of DES called the Improved DES which exhibits that the improved DES is secure than the DES against differential cryptanalysis. It divides each substitution box into four sub blocks of 16 bits and then executes the zig-zag function of each of the 4-sub blocks. It improves the standard encryption levels by columnar transposition.

Impact of Emerging Hardware on Security and Privacy

The articles in this special issue focus on ongoing research efforts in the development, use, and evaluation of emerging hardware features and techniques to improve system security. We have seen the emergence of new hardware features to improve software security by limiting memory access within an address space, such as Intel’s memory protection keys (MPKs) and extended page table (EPT) switching, among others. Researchers have also proposed hardware-assisted roots of trust using trusted execution environments (TEEs), such as Intel’s software guard extensions (SGXs), so some questions are what the utility is of such techniques and what limitations they still face. The papers in this special issue provide guidance in these areas, which can be helpful in examining future research directions and future applications of such techniques.

Effectiveness of combined methods of treatment of atrophic scars of skin

Atrophic post-acne scars are severe manifestations of the post-acne symptom complex, which occur as a result of inflammation due to the loss of collagen fibers and subcutaneous fat during the healing of inflammatory acne elements. The incidence of post-acne scars has been little studied, but according to some authors, about 90 % of patients with severe and moderate acne suffer from them. The aim of the study was to study the effect of the combined use of IPL- and PRP-therapy, fractional microneedle radiofrequency therapy and PRP-therapy, as well as microneedling of autologous plasma with cells on the clinical symptoms of atrophic skin scars, taking into account the duration of the pathological process, based on the dynamics of the indicators of the Manchester Assessment Scale of Scar Deformations. Complex methods showed higher efficiency than the use of hardware techniques in relation to all clinical signs of atrophic scars.

Application of noninvasive hardware technique of transcranial electrical stimulation (TES) in patients with atopic dermatitis

BACKGROUND: Transcranial electrostimulation (TES therapy) is a physiotherapeutic non-invasive technology that allows painlessly, selectively and strictly dosed activation of certain brain structures producing endogenous opioid peptides by modulating activity in these brain regions that are involved in the processing of pain and itching signals. Originally, this technique was used as an electronarcosis, then the scope of TES therapy in medicine expanded and began to be used in neurology, gynecology, urology, gastroenterology. The efficacy of its use in dermatology as an antipruritic action remain poorly understood.
 AIM: the purpose of the pilot study was to evaluate the effectiveness and acceptability of the use of non-invasive TES therapy in patients with atopic dermatitis.
 MATERIAL AND METHODS: According to the existing concept, itching is considered as a kind of pain sensation caused by an altered threshold level of nerve endings in the skin that perceive pain. Given this similarity of the mechanisms of pain and itching, noninvasive brain stimulation can simultaneously reduce itching, which opens up broad prospects for its further use in itchy dermatoses.
 A pilot controlled study was conducted, the object of the study was patients with moderate atopic dermatitis (9 women and 6 men) aged 19 to 48 years. TES therapy was carried out by a pulsed bipolar electrical stimulator with acoustic effect "TRANSAIR-07" according to the standard method. All patients who received a course of TES monotherapy underwent 10 procedures, 1 time a day, lasting 3040 minutes, with a current of 13 mA. To assess the dynamics of clinical manifestations of atopic dermatitis and the severity of the disease, the following scales were used: SCORAD, EASI, IGA, DLQI, HADS, BDI. The assessment was carried out twice: the first time on the day of diagnosis verification and again on the 10th day of treatment.
 RESULTS: As a result of the treatment, there was a decrease in itching from 5 to 2 points, an improvement in mood from 5 to 1 point, an increase in performance from 4 to 1 point, a decrease in anxiety and depression from 4 to 2 points. With a general assessment of the dynamics of the indicators of the applied clinical scales (SCORAD, EASI, IGA, DLQI, HADS, BDI), the total average effectiveness in the form of parameter improvement amounted to 66.25% reduction by the end of the 10-day course.
 CONCLUSION: The work presents the modern aspects of the application of non-invasive hardware transcranial electrical stimulation technique. The positive results of our own pilot study are presented and data confirming the clinical efficacy of transcranial electrical stimulation in patients with atopic dermatitis are presented, which determines the prospect of developing a combined application of TES therapy for this persistent disease.

A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm

The energy efficiency of deep neural network (DNN) inference can be improved with custom accelerators. DNN inference accelerators often employ specialized hardware techniques to improve energy efficiency, but many of these techniques result in catastrophic accuracy loss on transformer-based DNNs, which have become ubiquitous for natural language processing (NLP) tasks. This article presents a DNN accelerator designed for efficient execution of transformers. The proposed accelerator implements per-vector scaled quantization (VSQ), which employs an independent scale factor for each 64-element vector to enable the use of 4-bit arithmetic with little accuracy loss and low energy overhead. Using a multilevel dataflow to maximize reuse, the 5-nm prototype achieves 95.6 tera-operations per second per Watt (TOPS/W) at 0.46 V on a 4-bit benchmarking layer with VSQ. At a nominal voltage of 0.67 V, the accelerator achieves 1734 inferences/s/W (38.7 TOPS/W) with only 0.7% accuracy loss on BERT-Base and 4714 inferences/s/W (38.6 TOPS/W) with 0.15% accuracy loss on ResNet-50 by using quantization-aware fine-tuning to recover accuracy, demonstrating a practical accelerator design for energy-efficient DNN inference.

Concurrency and Operating Systems, Processors, and Programming Languages

The main content of our research is to optimize and improve the concurrency of the system from the three aspects of operating system, processor and programming language. Previous solutions have focused on hardware techniques to improve concurrency. Our goal is to solve the problem of resource occupancy and contention through algorithms, data structures, optimization interrupts, and optimization of process and coroutine scheduling. In the final ideal case, the finite-state machine switching code generated by the compiler can be used to switch the coroutines in the same process. For threads, the thread code provided by the kernel does the switching. The asynchronous system call is optimized to automatically generate the corresponding system call request code according to the execution compiler and maintain the coroutine control block data structure. This is also the most difficult part of this research.

Aperture–patch sandwich metasurface for magnetic field enhancement in 1.5 T MRI

Magnetic resonance imaging (MRI) is an increasingly popular non-invasive technique for clinical diagnosis. Signal-to-noise ratio (SNR) is a crucial performance metric of MRI, improvement of which can be exchanged for increased image resolution or decreased scan time. Besides the progress in various hardware and software techniques for improving SNR in MRI scanners, use of metasurfaces as accessories has recently shown potential towards enhancing SNR by boosting local magnetic field in the scanned volume. Magnetic field enhancement over a larger depth from the skin is essential for imaging of deeper tissues, which can be facilitated by a specifically designed metasurface. Here we present such a metasurface with complementary-type resonant structures on the two sides of a high-permittivity dielectric, which substantially increases magnetic flux density on the skin (forty-five fold) that decays down to unity at a depth of 95 mm from the skin. This results in boosting of SNR up to forty-fold on the skin in 1.5 T MRI, while keeping tissue heating below the safety limit. An original analytical approach is formulated to readily estimate the SNR enhancement factor of this metasurface. Using the designed metasurface as an accessory for MRI scanners could help making MRI scans more efficient and affordable.

Dual-use baseband signal design for RadCom with position index and phase modulation

This paper deals with the synthesis of dual-use signal to simultaneously achieve detection and communication for a dual-function radar communication (DFRC) system. A new position index and phase (PIP) modulation is proposed for information embedding, where position index modulation is realized by selecting the information embedding subpulses (InSp) positions. Moreover, differential phase modulation is utilized to distinguish InSp from other available subpulses. As for radar detectability, peak sidelobes level (PSL) of signal auto-correlation function is minimized. Meanwhile, constant-envelope and phases constraints are forced to ensure compatibility with the current hardware technique and modulation demand, respectively. To tackle the resulting nonconvex and NP-hard optimization problem, a spectral majorization-minimization (SM) dual-use baseband signal design method is proposed. In each iteration, the original problem is approximated by a tractable problem with a closed-form solution. Finally, simulation and semi-physical experimental results demonstrate the designed dual-use signal is capable of realizing high data rate communication and ensuring acceptable radar performance.

Protocols on correction of signs of undifferentiated connective tissue dysplasia using hardware and injection techniques in cosmetic patients

Undifferentiated connective tissue dysplasia (UCTD) is a genetically determined condition with a progressive course characterized by defects in the fibrous structures and the basic connective tissue substance, leading to impaired formation of organs and systems, which determines the features of the associated pathology, as well as the pharmacokinetics and pharmacodynamics of drugs. In dermatological and cosmetological practice, the issue of UCTD is very topical, as individual external presentations of the connective tissue dysmorphogenesis among young people can reach 85.4%. In recent decades, a real revolution has taken place in aesthetic medicine, which is associated with the emergence of new injectable products, development of hardware, cellular and thread techniques. Every year mew methods of treatment emerge and the existing ones are improved. Statistics of the past years show that minimally invasive non-surgical interventions are becoming more and more popular in all countries, their number significantly exceeds the number of surgical operations. To date, there are 2 groups of methods aimed at collagen stimulation: hardware techniques and injection methods. The effectiveness of the use of polylactic acid drugs as the first-line drugs for the correction of UCTD in order to prevent premature ageing was evaluated in the presented clinical cases. Patients with a cosmetology profile were treated according to the complex protocols that included amino acid replacement therapy, IV therapy, contour correction with drugs containing polylactic acid and hardware techniques (microneedle RF lifting with insulated and non-insulated needles, ultrasonic SMAS lifting, ablative laser techniques, Ipl-therapy). As a result, an algorithm for managing a patient with UCTD was developed in the cosmetology practice. The provided clinical cases show that the detection of signs of UCTD can significantly improve the patient’s quality of life and prevent premature aging.

Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, although enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the combination of Edge and approximate computing techniques that allows for data processing nearer to the user. This article aims to survey the potential benefits of these paradigms’ intersection. We provide a state-of-the-art review of circuit-level and architecture-level hardware techniques and popular applications. We also outline essential future research directions.

Enhanced Multicore Performance Using Novel Thread-Aware Cache Coherence and Prefetch-Control Mechanism

We propose a hardware technique for cache coherence over the existing approaches that ensure that shared and less frequently used cache blocks bypass private caches of multiple cores. Furthermore, this manuscript proposes a mechanism to tune the aggressiveness of a data prefetcher. Increased cache hit rate and improved performance have been observed since coherence management and prefetching delays are avoided using the proposed bypassing and thread progress-aware prefetch controlling mechanism. Our approach shows around 19% improvement in cache hit rate and 29% average performance improvement over existing state-of-the-art techniques for Parsec & Splash-2 multithreaded benchmarks.

Cramér–Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems

As radio frequency (RF) hardware continues to improve, many technologies that were traditionally impractical have suddenly become viable alternatives to legacy systems. Two-way ranging (TWR) is often considered a poor positioning solution for airborne and other vehicular navigation systems due to its low precision, poor angular resolution, and precise timing requirements. With the advent of modern RF hardware and advanced processing techniques, however, modern studies have experimentally demonstrated TWR systems with an unprecedented, sub-centimeter ranging precision with low size, weight, power, and cost (SWaP-C) consumer-grade hardware. This technique enables a new class of positioning, navigation, and timing (PNT) capabilities for urban and commercial aircraft but also instigates new system design challenges such as antenna placement, installation of new electronics, and design of supporting infrastructure. To inform these aircraft design decisions, we derive 2D and 3D Cramér–Rao lower bounds (CRLBs) on position and orientation estimation in a multi-antenna TWR system. We specifically formulate these bounds as a function of the number of antennas, platform geometry, and geometric dilution of precision (GDoP) to inform aircraft design decisions under different mission requirements. We simulate the performance of several classic position and orientation estimators in this context to validate these bounds and to graphically depict the expected performance with respect to these design considerations. To improve the accessibility of these highly theoretical results, we also present a simplified discussion of how these bounds may be applied to common airborne applications and suggest best practices for using them to inform aircraft design decisions.

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) "Healthy" or "COPD" and (2) "Healthy", "COPD", or "Pneumonia" classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.

Hyperpolarized 129Xe MRI at low field: Current status and future directions

Magnetic Resonance Imaging (MRI) is dictated by the magnetization of the sample, and is thus a low-sensitivity imaging method. Inhalation of hyperpolarized (HP) noble gases, such as helium–3 and xenon-129, is a non-invasive, radiation-risk free imaging technique permitting high resolution imaging of the lungs and pulmonary functions, such as the lung microstructure, diffusion, perfusion, gas exchange, and dynamic ventilation. Instead of increasing the magnetic field strength, the higher spin polarization achievable from this method results in significantly higher net MR signal independent of tissue/water concentration. Moreover, the significantly longer apparent transverse relaxation time T2* of these HP gases at low magnetic field strengths results in fewer necessary radiofrequency (RF) pulses, permitting larger flip angles; this allows for high-sensitivity imaging of in vivo animal and human lungs at conventionally low (<0.5 T) field strengths and suggests that the low field regime is optimal for pulmonary MRI using hyperpolarized gases.In this review, theory on the common spin-exchange optical-pumping method of hyperpolarization and the field dependence of the MR signal of HP gases are presented, in the context of human lung imaging. The current state-of-the-art is explored, with emphasis on both MRI hardware (low field scanners, RF coils, and polarizers) and image acquisition techniques (pulse sequences) advancements. Common challenges surrounding imaging of HP gases and possible solutions are discussed, and the future of low field hyperpolarized gas MRI is posed as being a clinically-accessible and versatile imaging method, circumventing the siting restrictions of conventional high field scanners and bringing point-of-care pulmonary imaging to global facilities.

Workflow assessment of an augmented reality application for planning of perforator flaps in plastic reconstructive surgery: Game or game changer?

In contrast to the rising amount of financial investments for research and development in medical technology worldwide is the lack of usability and clinical readiness of the produced systems. We evaluated an augmented reality (AR) setup under development for preoperative perforator vessel mapping for elective autologous breast reconstruction. In this grant-supported research pilot, we used magnetic resonance angiography data (MR-A) of the trunk to superimpose the scans on the corresponding patients with hands-free AR goggles to identify regions-of-interest for surgical planning. Perforator location was assessed using MR-A imaging (MR-A projection) and Doppler ultrasound data (3D distance) and confirmed intraoperatively in all cases. We evaluated usability (System Usability Scale, SUS), data transfer load and documented personnel hours for software development, correlation of image data, as well as processing duration to clinical readiness (time from MR-A to AR projections per scan). All perforator locations were confirmed intraoperatively, and we found a strong correlation between MR-A projection and 3D distance measurements (Spearman r = 0.894). The overall usability (SUS) was 67 ± 10 (=moderate to good). The presented setup for AR projections took 173 min to clinical readiness (=availability on AR device per patient). In this pilot, we calculated development investments based on project-approved grant-funded personnel hours with a moderate to good usability outcome resulting from some limitations: assessment was based on one-time testing with no previous training, a time lag of AR visualizations on the body and difficulties in spatial AR orientation. The use of AR systems can provide new opportunities for future surgical planning, but has more potential for educational (e.g., patient information) or training purposes of medical under- and postgraduates (spatial recognition of imaging data associated with anatomical structures and operative planning). We expect future usability improvements with refined user interfaces, faster AR hardware and artificial intelligence-enhanced visualization techniques.

Development of a low-frequency magnetic lightning mapping system (LFM-LMS) in North China: validation and preliminary results

A low-frequency magnetic lightning mapping system (LFM-LMS) was built during the SHAndong Triggered Lightning Experiment (SHATLE), based on continuous measurements of magnetic field radiation from lightning. The hardware and source-mapping techniques used by the LFM-LMS were introduced; both Monte Carlo simulations and the observation of rocket-triggered lightning examples were employed to examine the location accuracy and detection effectiveness of the LFM-LMS. We estimated that the system’s location accuracy about 100−200 m horizontally and ~200 m vertically. A natural intra-cloud lightning flash and a rocket-triggered lightning flash, both with intricate structures and discharging processes, were examined using the three-dimensional mapping results. The progressing path of negative lightning leaders is usually well-defined, and its propagation speed is estimated to be (0.5−1.4) × 10⁶ m/s. In summary, the LFM-LMS can reconstruct the three-dimensional morphology of lightning flashes; this technology provides a efficient method for investigating the characteristics of lightning development, as well as the overall electrical strucuture of thunderstorms.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations.

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.