Medeli Electronics (Shanghai) Co., Ltd.

Nanjing Iluvatar Corex Technology Co., Ltd.

Wangsu Science & Technology Co., Ltd.

Jiejun Chen

Peng Zhou

Pingping Shao

Yongliu Wang

Guozong Liu

Xinghua Ge

Jianwei Deng

The present disclosure proposes a keybed device, including: a keybed support; a keybed arranged on the keybed support; a key height limiting column arranged in one-to-one correspondence with each key in the keybed, and an end of the key height limiting column is fixed on a bottom surface of the corresponding key; a pressure detecting device arranged in one-to-one correspondence under each key height limiting column to detect a pressing strength of each key height limiting column; and a flexible support structure arranged below each key height limiting column and located on at least one side of each pressure detection device. The keybed device includes a flexible support structure, which can avoid false triggering of the Aftertouch effect. A same depth is pressed down from the flexible support structure to obtain a consistent Aftertouch effect, and the performance effect is controllable.

Keybed device

A hierarchical sparse tensor compression method based on artificial intelligence devices, in DRAM, not only saves the storage space of the neuron surface, but also adds a meta-surface to the mask block. When reading data, the mask is first read, then the size of the non-zero data is calculated, and only these non-zero data are read to save DRAM bandwidth. In the cache, only non-zero data is stored, so the required storage space is reduced. When processing data, only non-zero data is used. The method uses a bit mask to determine if the data is zero. There are three levels in the hierarchical compression scheme: tiles, lines, and points, reading bitmasks and non-zero data from DRAM, and saving bandwidth by not reading zero data. When processing data, if their bit mask is zero, the tile data may be easily removed.

Hierarchical sparse tensor compression method in artificial intelligent devices

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Shanghai, China

Jiejun Chen