Abstract: This application describes chiplet-based neuromorphic systems, devices, and chips. An exemplary chiplet-based neuromorphic system may include: a plurality of neuron processing entities (NPE); a plurality of chiplets each comprising a plurality of switches and a group of NPEs from the plurality of NPEs; and a plurality of interposers each comprising a plurality of routers and a group of chiplets from the plurality of chiplets; wherein each of the plurality of switches within each chiplet connects to one or more of the group of NPEs, and the plurality of switches within the each chiplet are organized in a tree topology; wherein each of the plurality of routers within each interposers connects to one or more of the group of chiplets, and the plurality of routers within the each interposer are organized in a tree topology.

Abstract: The present invention discloses an image processing method and apparatus, a storage medium, and an electronic device. The method includes: obtaining a to-be-processed image and sensing data corresponding to the to-be-processed image; obtaining region-of-interest information of the to-be-processed image based on the sensing data; determining a first image region and a second image region of the to-be-processed image based on the region-of-interest information, wherein the first image region is an image region determined based on the region-of-interest information, and the second image region is an image region other than the first image region in the to-be-processed image; and processing the first image region in a first processing manner, and processing the second image region in a second processing manner, wherein computation complexity of the first processing manner is higher than that of the second processing manner.

Abstract: This disclosure provides a computation unit, a related apparatus, and a method. The computation unit includes: a weight buffer adapted to store a row vector fetched from an M×K? sparsified weight matrix, where M and K are respectively a number of rows and a number of columns of the weight matrix before being sparsified, and ? is a sparsity coefficient; an excitation buffer adapted to store a K×N excitation matrix; an index selector adapted to store a selection index corresponding to the row vector, and select a row of the excitation matrix based on the selection index, to obtain a K?×N selected excitation matrix; and a dot product computation unit adapted to multiply the row vector by the selected excitation matrix. This disclosure implements a manner of running a DNN on hardware. In such a manner, structured sparsity of a DNN can be fully utilized, so that inference efficiency is improved; moreover, a register file occupies relatively small bandwidth, and a timing constraint is weak.

Abstract: The present disclosure discloses a method and a computing device for processing an input vector sequence to generate an output vector. The method comprises processing one or more input vectors of the input vector sequence to generate one or more sub-vectors respectively, the input vector sequence including a plurality of input vectors including the one or more input vectors arranged in a sequence, wherein the plurality of input vectors and the output vector characterize features of an object, and a respective input vector is processed by processing one or more input vectors adjacent to the respective input vector in the input vector sequence using a machine learning method to generate a corresponding sub-vector; and after sequentially generating the one or more corresponding sub-vectors for the one or more input vectors in the input vector sequence to form a sub-vector sequence including the one or more sub-vectors, generating the output vector based on the sub-vector sequence.

Abstract: The present disclosure discloses a cleaning device and a clean method, the cleaning device includes a sweeping module and a washing module, the sweeping module includes a brush component and a steam generating component, the steam generating component and the brush component are arranged in sequence in the cleaning direction of the cleaning device; the washing module is configured to wash the parts to be cleaned, the washing module and the sweeping module are arranged in sequence in the cleaning direction of the cleaning device.

Abstract: The present disclosure discloses a cleaning device and a clean method, the cleaning device includes a sweeping module and a washing module, the sweeping module includes a brush component and a steam generating component, the steam generating component and the brush component are arranged in sequence in the cleaning direction of the cleaning device; the washing module is configured to wash the parts to be cleaned, the washing module and the sweeping module are arranged in sequence in the cleaning direction of the cleaning device.