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News:
In their paper, Prof. Cui, Ma and their colleagues showed that the same architecture also performs well on other tasks, for instance classifying handwritten digits with high levels of accuracy. In their next works, they plan to evaluate SPNN on other tasks, while also increasing its complexity so that it can tackle more advanced problems.
“The prototype implemented in this work is based on a 4×4 fully neural network, which is relatively low,” Ma added. “The SPNN structure resembles a circuit system, which means that the scale of SPNN layers could be enlarged without increasing the footprint of the device. For example, a cubic structural form could be created to reduce the physical size of the partially connected system, achieving ultra-high spatial utility in three-dimensional space. Additionally, we can also reduce the size of the network by improving the operating frequency band.”
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