Abstract:
Lossy tensor compression and decompression circuits compress and decompress tensor elements based on the values of neighboring tensor elements. The lossy compression circuit scales each decompressed tensor element of a tile by a scaling factor that is based on the maximum value that can be represented by the number of bits used to represent a compressed tensor element, and the greatest value and least value of the tensor elements of the tile. The lossy decompression circuit performs the inverse of the lossy compression. The compression circuit and decompression circuit have parallel multiplier circuits and parallel adder circuits to perform the lossy compression and lossy decompression, respectively.
Abstract:
A system includes an integrated circuit configured to communicating data in a channel. A channel matrix for the channel including a plurality of columns is received. A preprocessing step is performed, using a preprocessing unit, to compute a plurality of preprocessed column values corresponding to respective columns. An update step is performed, using an update unit, to update an estimation vector using a plurality of outer-loop iterations of an outer loop. Each outer-loop iteration updates the estimation vector using the plurality of preprocessed column values. An access link process is performed using the estimation vector.
Abstract:
An apparatus relating generally to matrix inversion is disclosed. This apparatus includes a matrix inversion module coupled to receive matrix information and to provide an approximation of an inversion of the matrix information. The matrix inversion module comprises a decomposition block coupled to receive the matrix information and to decompose the matrix information into diagonal matrix information and off diagonal matrix information, and an expansion block. The expansion block is coupled to receive the diagonal matrix information and the off diagonal matrix information, and to invert a matrix sum of the diagonal matrix information and the off diagonal matrix information by generation of a portion of a series expansion.
Abstract:
A method includes communicating data in a channel. Received symbols for the data correspond to points of a received symbol space respectively. First and second dimensions of the received symbol space correspond to a real part and an imaginary part of the received symbols respectively. A first received symbol for the data is obtained. A first region of the received symbol space for the first received symbol is determined. A first regression model associated with the first region and a first bit of the first received symbol is retrieved from a storage. The first regression model includes a plurality of regressors. A first log-likelihood ratio (LLR) for the first bit of the first received symbol is estimated using the first regression model.
Abstract:
An integrated circuit (IC) includes a downlink unit including an input to receive a first plurality of frequency domain (FD) symbols associated with data symbols for a plurality of users, and an iteration unit to perform a plurality of iterations based on adjustment values. Each iteration includes generating a second plurality of FD symbols by performing a precoding process based on the first plurality of FD symbols, generating a third plurality of time domain (TD) symbols by performing a first modulation process based on the second plurality of FD symbols, generating a fourth plurality of TD symbols by performing a dynamic range reduction process based on absolute values of the third plurality of TD symbols, and updating the adjustment values. The downlink unit further includes a decision unit configured to generate transmit TD symbols for transmission through a channel to the plurality of users.
Abstract:
A system includes a memory and an integrated circuit coupled to the memory. The integrated circuit is configured to communicate data in a channel characterized as a space having at least a frequency dimension. Anchor locations within the space correspond to respective regions of the space. The integrated circuit is further configured to determine a first inverse of a first matrix that corresponds to a first channel matrix for a first anchor location of the anchor locations. The first anchor location corresponds to a first region of the regions. The integrated circuit is further configured to perform an access link process for a second location other than the first anchor location but within the first region, the access link process using the first inverse determined for the first anchor location.
Abstract:
Method and system relating generally to convolution is disclosed. In such a method, an image patch is selected from input data for a first channel of a plurality of input channels of an input layer. The selected image patch is transformed to obtain a transformed image patch. The transformed image patch is stored. Stored is a plurality of predetermined transformed filter kernels. A stored transformed filter kernel of the plurality of stored predetermined transformed filter kernels is element-wise multiplied by multipliers with the stored transformed image patch for a second channel of the plurality of input channels different from the first channel to obtain a product. The product is inverse transformed to obtain a filtered patch for the image patch.
Abstract:
Circuits and method for multiplying floating point operands. An exponent adder circuit sums a first exponent and a second exponent and generates an output exponent. A mantissa multiplier circuit multiplies a first mantissa and a second mantissa and generates an output mantissa. A first conversion circuit converts the output exponent and output mantissa into a fixed point number. An accumulator circuit sums contents of an accumulation register and the fixed point number into an accumulated value and stores the accumulated value in the accumulation register.
Abstract:
Disclosed approaches for convolving input feature maps in a neural network include a circuit arrangement circuit that includes memory circuitry and convolution circuitry. The memory circuitry is configured to store K NxN first filters, and C 1x1 second filters, wherein N ≥ 1, and 1
Abstract:
Circuits and method for multiplying floating point operands. An exponent adder circuit sums a first exponent and a second exponent and generates an output exponent. A mantissa multiplier circuit multiplies a first mantissa and a second mantissa and generates an output mantissa. A first conversion circuit converts the output exponent and output mantissa into a fixed point number. An accumulator circuit sums contents of an accumulation register and the fixed point number into an accumulated value and stores the accumulated value in the accumulation register.