Logic Design of Neural Networks for High-Throughput and Low-Power Applications

Neural networks (NNs) have been successfully deployed in various fields. In NNs, a large number of multiplyaccumulate (MAC) operations need to be performed. Most existing digital hardware platforms rely on parallel MAC units to accelerate these MAC operations. However, under a given area constraint, the number of MAC units in such platforms is limited, so MAC units have to be reused to perform MAC operations in a neural network. Accordingly, the throughput in generating classification results is not high, which prevents the application of traditional hardware platforms in extreme-throughput scenarios. Besides, the power consumption of such platforms is also high, mainly due to data movement. To overcome this challenge, in this paper, we propose to flatten and implement all the operations at neurons, e.g., MAC and ReLU, in a neural network with their corresponding logic circuits. To improve the throughput and reduce the power consumption of such logic designs, the weight values are embedded into the MAC units to simplify the logic, which can reduce the delay of the MAC units and the power consumption incurred by weight movement. The retiming technique is further used to improve the throughput of the logic circuits for neural networks. In addition, we propose a hardware-aware training method to reduce the area of logic designs of neural networks. Experimental results demonstrate that the proposed logic designs can achieve high throughput and low power consumption for several high-throughput applications.Comment: accepted by ASPDAC 202

A COMPARISON OF EMG AND KINEMATIC ANALYSIS BETWEEN GROUND AND TREADMILL RUNING FOR CHINESE ELITE SPRINTER-PU FAN FANG

Ms. Pu Fan-fang, a Chinese National championship, has been training on simulated treadmill for 4 years to improve her ability of velocity endurance. The purpose of the present study was to compare the changes of her movement structures in ground and treadmill running. EMG and' kinematical analysis were used in the test. The kinematical data results show that significant differences were noted between the two conditions for the take off angle, minimum knee angle of swing leg, the minimum angle between thigh and horizontal line, soar high and soar time. The EMG result revealed that the obvious differences of EMG distribution of eight muscles existed in the two conditions. According to the testing results, it should be considered that more using treadmill training could influence her movement structure although it is a good method to improve velocity endurance

Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a meticulously curated, comprehensive instruction dataset expressly designed for the biomolecular realm. Mol-Instructions is composed of three pivotal components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions, each curated to enhance the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on the representative LLM, we underscore the potency of Mol-Instructions to enhance the adaptability and cognitive acuity of large models within the complex sphere of biomolecular studies, thereby promoting advancements in the biomolecular research community. Mol-Instructions is made publicly accessible for future research endeavors and will be subjected to continual updates for enhanced applicability.Comment: Project homepage: https://github.com/zjunlp/Mol-Instructions. Add quantitative evaluation

Effect of fiber type and content on mechanical properties of microbial solidified sand

Fibers are applied to construction works to improve the strength and brittle failure of the soil. In this paper, fibers with a length of 6 mm are added to the microbial cemented sand, and fiber types and content are research variable. Unconfined compressive strength (UCS), permeability coefficient, water absorption rate, dry density, and calcium carbonate precipitation of the solidified sand were tested. The physical and mechanical properties of fiber types and content on the immobilization of microorganisms were also analyzed from the micro–macro perspective. Results are presented as follows. The UCS of the Microbial induced calcium carbonate precipitation (MICP) treated sand increases first and then decreases with the increasing fiber content. This phenomenon is due to the promotion of calcium carbonate precipitation by short fiber reinforcement, the limited movement of the sand particles caused by the formed network between the fibers, and the enhanced strength of the microbial solidified sand. However, the agglomeration caused by additional fibers leads to the uneven distribution of calcium carbonate and the reduction in strength. The optimum fiber contents of polypropylene, glass, polyvinyl alcohol, and basalt fibers are 0.4%, 0.2%, 0.2%, and 0.1%, respectively

Experimental Study, Characterization and Application of Starch-Graft-Acrylamide Gel for Plugging

During underbalance drilling, completion and workover wells, plugging channeling, blocking preformation and plugging formation water are inevitable problems. Gel is one of the most effective and convenient method to solve the problem. In this study, modified starch gel is synthesized, investigated experimentally and improved for efficient oil and gas field applications. The gel slurry is composed of starch (3.6 wt.%), initiator (0.02 wt.%), acrylamide (14.4 wt.%), cross-linking agent (4.7 wt.%), all of the components are mixed together with water at pH 10 – 11 which viscosity is as low as 35 – 82 mPa.s and desired to form gel. Here the effects of the components, reaction temperature and pH on gelation time and gel viscosity are systematically investigated, and the results showed that the gelation can be controlled in a wide range 30 – 120 min efficiently by pH and initiator. Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) are employed to study the molecular structure and microstructure of the gel, respectively. A compact three-dimensional network structure was formed in the gel, which contribute to a good adhesion. The gel has been successfully used in shale gas field which provides a reference for sealing other similar high formation pressure under unbalanced workover treatment. DOI: http://dx.doi.org/10.5755/j01.ms.24.4.18565</p

EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to the outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners to apply knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily apply to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub at https://github.com/zjunlp/EasyEdit, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video at http://knowlm.zjukg.cn/easyedit.mp4.Comment: The project website is https://github.com/zjunlp/EasyEdi

Effect of Ultrasonic Surface Rolling Process on Surface Properties and Microstructure of 6061 Aluminum Alloy

Nano-surface layers were prepared on the surface of 6061 aluminum alloy using the ultrasonic surface rolling process (USRP). The surface morphology, surface roughness, microstructure, hardness, and corrosion resistance of 6061 aluminum alloy were systematically characterized using X-ray diffraction (XRD), laser scanning confocal microscopy (LSCM), optical microscope(OM), scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), and other testing methods. The results showed that ultrasonic surface rolling strengthening did not change the surface phase composition of 6061 aluminum alloy. It changed the size of the surface phases and the distance between the phases while refining the surface grains. The static pressures has a great influence on the surface properties of 6061 aluminum alloy. The best surface properties were obtained under 500N static pressures. The surface hardness reached 129.5HV0.5, the surface morphology was flat and continuous, the surface roughness was reduced to Ra0.191μm, and the corrosion resistance was significantly improved

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients