Abstract: A signed bit slice generator includes a divider configured to divide input data, which is 2's complement data having N (where N is a natural number)-bit precision, and divide remaining bits excluding a sign bit of the input data into a predetermined number of bit slices, a sign bit adder configured to add a sign bit to each of the bit slices, a sign value setter configured to set a sign bit of an MSB slice among the bit slices to a sign value of the input data and to set sign bits of the remaining bit slices to positive sign values, and a sparse data compressor configured to perform sparse data compression on each of the signed bit slices, thereby generating a predetermined number of signed bit slices having the same number of bits where each bit slice includes a sign bit.

Abstract: Disclosed is a conjugate gradient acceleration apparatus using band matrix compression in depth fusion technology including a band matrix conversion unit configured to convert an adjacency matrix for correcting depth data acquired from data of an image sensor through deep learning based on depth information acquired from a depth sensor into a band matrix using rows of the adjacency matrix as addresses of query points and columns of the adjacency matrix as the nearest neighbors at the query points, a band matrix compression unit configured to mark an index on each band in order to compress the band matrix and to compress data, a memory unit configured to store tile data of the band matrix, and a band matrix calculation unit configured to perform computation of the band matrix and a transposed band matrix or computation of a symmetric band matrix with respect to the band matrix and a vector.

Abstract: Disclosed is a 3D point cloud-based deep learning neural network acceleration apparatus including a depth image input unit configured to receive a depth image, a depth data storage unit configured to store depth data derived from the depth image, a sampling unit configured to sample the depth image in units of a sampling window having a predetermined first size, a grouping unit configured to generate a grouping window having a predetermined second size and to group inner 3D point data by grouping window, and a convolution computation unit configured to separate point feature data and group feature data, among channel-direction data of 3D point data constituting the depth image, to perform convolution computation with respect to the point feature data and the group feature data, to sum the results of convolution computation by group grouped by the grouping unit, and to derive the final result.