We demonstrate the use of ball milling mechanochemistry for rapid, simple, and materials-efficient synthesis of the organic mineral paceite CaCu(OAc) 4 ·6H 2 O (where OAc – is the acetate ion), composed of coordination polymer chains containing alternating Ca 2+ and Cu 2+ ions, as well as its cadmium-based analogue CaCd(OAc) 4 ·6H 2 O. While the synthesis of paceite in aqueous solutions requires a high excess of the copper precursor, mechanochemistry permits the use of stoichiometric amounts of reagents, as well as the use of poorly soluble and readily accessible calcium carbonate or hydroxide reactants. As established by thermochemical measurements, enthalpies of formation of both synthetic paceite and its cadmium analogue relevant to the mechanochemical reactions are highly exothermic. Reactions can also be conducted using accelerated aging, a synthetic technique that mimics geological processes of mineral weathering. Accelerated aging reactivity involving copper(II) acetate monohydrate (hoganite) and calcium carbonate (calcite) provides a potential explanation of how complex organic minerals like paceite could form in a geological environment.

Artificial intelligence has developed rapidly in recent years. Deep neural networks are the basis of many artificial intelligence applications. How to accelerate the computational processing of deep neural networks is very important. To explor the potential for accelerating the process deep neural networks on various hardware platforms, we propose a convolutional neural network optimization method based on the Weight-Stationary for SW26010 processor. We re-circulate convolution loops and use hybrid DMA transmission mode to increase memory bandwidth and reduce memory access overhead. On top of those, further optimizations are done based on register communication, asynchronous DMA transfer double buffering, instruction scheduling and other schemes. Finally, we achieve a double-precision convolution performance over 2.4 Tflops, achieving 81% of the processor's peak performance. In multiple parameters, we achieve a proforamnce acceleration of 2.4 − 4.0× speedup compared to the Tesla K80 GPU with cuDNNv7.

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