An embodiment of the invention is a processor including execution circuitry to, in response to a decoded instruction, convert a half-precision floating-point value to a single-precision floating-point value and store the single-precision floating-point value in each of the plurality of element locations of a destination register. The processor also includes a decoder and the destination register. The decoder is to decode an instruction to generate the decoded instruction.

The present disclosure provides a display module. The module includes a module support structure, a plurality of light-emitting elements coupled to the module support structure, and one or more latch assemblies configured to removeably couple the module support structure to a support chassis. Each of the one or more latch assemblies includes a first magnet movably coupled with the module support structure at a first location. The assemblies further include a second magnet movably coupled with the module support structure at a second location. The second magnet is movable relative to the module support structure between a first corresponding position and a second corresponding position. The assemblies further include a shaft connecting the first magnet and the second magnet so that the first magnet and the second magnet move together.

In an embodiment, a computation engine may perform dot product computations on input vectors. The dot product operation may have a first operand and a second operand, and the dot product may be performed on a subset of the vector elements in the first operand and each of the vector elements in the second operand. The subset of vector elements may be separated in the first operand by a stride that skips one or more elements between each element to which the dot product operation is applied. More particularly, in an embodiment, the input operands of the dot product operation may be a first vector having second vectors as elements, and the stride may select a specified element of each second vector.

In a very long instruction word (VLIW) central processing unit instructions are grouped into execute packets that execute in parallel. A constant may be specified or extended by bits in a constant extension instruction in the same execute packet. If an instruction includes an indication of constant extension, the decoder employs bits of a constant extension instruction to extend the constant of an immediate field. Two or more constant extension slots are permitted in each execute packet, each extending constants for a different predetermined subset of functional unit instructions. In an alternative embodiment, more than one functional unit may have constants extended from the same constant extension instruction employing the same extended bits. A long extended constant may be formed using the extension bits of two constant extension instructions.

Aspects for vector operations in neural network are described herein. The aspects may include a vector caching unit configured to store a first vector and a second vector, wherein the first vector includes one or more first elements and the second vector includes one or more second elements. The aspects may further include one or more adders and a combiner. The one or more adders may be configured to respectively add each of the first elements to a corresponding one of the second elements to generate one or more addition results. The combiner may be configured to combine a combiner configured to combine the one or more addition results into an output vector.

Aspects for matrix multiplication in neural network are described herein. The aspects may include a master computation module configured to receive a first matrix and transmit a row vector of the first matrix. In addition, the aspects may include one or more slave computation modules respectively configured to store a column vector of a second matrix, receive the row vector of the first matrix, and multiply the row vector of the first matrix with the stored column vector of the second matrix to generate a result element. Further, the aspects may include an interconnection unit configured to combine the one or more result elements generated respectively by the one or more slave computation modules to generate a row vector of a result matrix and transmit the row vector of the result matrix to the master computation module.

Aspects for vector operations in neural network are described herein. The aspects may include a vector caching unit configured to store a first vector and a second vector, wherein the first vector includes one or more first elements and the second vector includes one or more second elements. The aspects may further include one or more adders and a combiner. The one or more adders may be configured to respectively add each of the first elements to a corresponding one of the second elements to generate one or more addition results. The combiner may be configured to combine a combiner configured to combine the one or more addition results into an output vector.

A plurality of software programmable processors is disclosed. The software programmable processors are controlled by rotating circular buffers. A first processor and a second processor within the plurality of software programmable processors are individually programmable. The first processor within the plurality of software programmable processors is coupled to neighbor processors within the plurality of software programmable processors. The first processor sends and receives data from the neighbor processors. The first processor and the second processor are configured to operate on a common instruction cycle. An output of the first processor from a first instruction cycle is an input to the second processor on a subsequent instruction cycle.

Aspects for vector operations in neural network are described herein. The aspects may include a vector caching unit configured to store a first vector and a second vector, wherein the first vector includes one or more first elements and the second vector includes one or more second elements. The aspects may further include one or more adders and a combiner. The one or more adders may be configured to respectively add each of the first elements to a corresponding one of the second elements to generate one or more addition results. The combiner may be configured to combine a combiner configured to combine the one or more addition results into an output vector.

A splicing screen, a method and a device for driving the splicing screen and a display apparatus are provided. The splicing screen includes at least one main display region configured to display a first portion of a display image, and a frame display region at edges of the main display region and configured to display a second portion of the display image other than the first portion. The method includes calculating a division manner for a display image; and displaying different portions of the image correspondingly on the main display region and the frame display region according to the division manner. The device for driving the splicing screen includes an image capture module and an image division display module.