Hardware implementations of Artificial Neural Networks (ANNs) using conventional binary arithmetic units are computationally expensive and energy-intensive together with large area footprints. Stochastic computing (SC) is an unconventional computing paradigm that operates on stochastic bit streams. It can offer low-power and area-efficient hardware implementations and has shown promising results when applied to ANN hardware circuits. SC relies on stochastic number generators (SNGs) to map input binary numbers to stochastic bit streams. The SNGs are conventionally implemented using random number generators (RNGs) and comparators. Linear feedback shifted registers (LFSRs) are typically used as the RNGs, which need far more area and power than the SC core, counteracting the latter's main advantages. To mitigate this problem, in this Letter, RNGs employing Spin–Orbit Torque (SOT)-induced stochastic switching of perpendicularly magnetized Ta/CoFeB/MgO nanodevices have been proposed. Furthermore, the SOT true random number generator (TRNG) is integrated with the simple CMOS stochastic computing circuits to perform a stochastic artificial neural network. To further optimize power and area efficiency, a fully parallel architecture and TRNG-sharing scheme are presented. The proposed stochastic ANN using the SOT-based TRNG obtains a negligible inference accuracy loss, compared with the binary version, and achieves 9× and 25× improvement in terms of area and power, respectively, compared with the ANN using LFSRs.
Analogue arithmetic operations are the most fundamental mathematical operations used in image and signal processing as well as artificial intelligence (AI). In-memory computing offers high performance and energy-efficient computing paradigm. To date, in-memory analogue arithmetic operation with emerging nonvolatile devices were usually implemented using discrete components, which limits the scalability and blocks large scale integration. Here, we experimentally demonstrate a prototypical implementation of in-memory analogue arithmetic operations (summation, subtraction and multiplication), based on in-memory electrical current sensing units using spinorbit torque (SOT) devices. The proposed analogue arithmetic operation structures are smaller than the state-of-the-art CMOS counterparts by several orders of magnitude.Moreover, data to be processed and computing results can be locally stored, or the analogue computing can be done in the nonvolatile SOT devices, which were exploited to experimentally implement image edge detection and signal amplitude modulation with simple structure. Furthermore, we constructed an artificial neural network (ANN) with SOT devices based synapses to realize pattern recognition with high accuracy of ~95%.
The threat of heavy metal pollution in water sources becomes increasingly serious. Toxic hexavalent chromium is harmful for health. Chitosan is a non-toxic and pollution-free substance, it is a cationic coagulant after protonated in diluted acid solution, it also can complex for many heavy metal ion. The study on removal of Cr (Ⅵ) and turbidity in drinking water by chitosan in this paper was performed. The results showed that the removal efficiency of Cr (Ⅵ) decreased with increasing pH, the removal rate of Cr (Ⅵ) was as high as 100% at pH4 using the chitosan concentration of 1.0g/L, but in these conditions, the turbidity was not removed. The removal efficiency of Cr (Ⅵ) bore relationship to stirring time of slow mixing step and initial concentration of Cr (Ⅵ). The best stirring time was 4 hours. To attain a good removal efficiency of Cr (Ⅵ), the dosage of chitosan must increase with the initial concentration of Cr (Ⅵ) increasing. At pH7, low chitosan dosage had good turbidity removal.
This paper analyzes the research characteristics and development context of carbon emissions in the steel industry, which will provide reference for future carbon emissions research and practice in the steel industry in China. Based on the “Web of Science Core Collection” (hereinafter referred to as SCIE) database, this paper used CiteSpace software to make a bibliometric analysis of the literatures related to carbon emissions in the steel industry from 1991 to 2020 in terms of countries and regions, major publishing institutions, high-yield authors, keywords and time zone views. The results show that: 1) Scholars and research institutions in different countries and regions form a close cooperation network according to their geographical location;2) The research forces with China, the United States, the United Kingdom and Japan as the core have provided key research support and contributed major research results to the carbon emission field of the steel industry;3) Climate change, production technology and future scenario analysis are the three main research topics;4) In the future research stage, the comprehensive application of various disciplines such as technical and economic analysis, scenario analysis and environmental climatology will become the forefront of the research on carbon emissions in the steel industry.
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