The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

A streaming engine employed in a digital signal processor specifies a fixed read only data stream. Once fetched the data stream is stored in two head registers for presentation to functional units in the fixed order. Data use by the functional unit is preferably controlled using the input operand fields of the corresponding instruction. A first read only operand coding supplies data from the first head register. A first read/advance operand coding supplies data from the first head register and also advances the stream to the next sequential data elements. Corresponding second read only operand coding and second read/advance operand coding operate similarly with the second head register. A third read only operand coding supplies double width data from both head registers.

Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.

The present disclosure includes apparatuses and methods for compute enabled cache. An example apparatus comprises a compute component, a memory and a controller coupled to the memory. The controller configured to operate on a block select and a subrow select as metadata to a cache line to control placement of the cache line in the memory to allow for a compute enabled cache.

Embodiments detailed herein relate to matrix operations. In particular, the loading of a matrix (tile) from memory. For example, support for a loading instruction is described in the form of decode circuitry to decode an instruction having fields for an opcode, a destination matrix operand identifier, and source memory information, and execution circuitry to execute the decoded instruction to load groups of strided data elements from memory into configured rows of the identified destination matrix operand to memory.

Disclosed embodiments relate to computing dot products of nibbles in tile operands. In one example, a processor includes decode circuitry to decode a tile dot product instruction having fields for an opcode, a destination identifier to identify a M by N destination matrix, a first source identifier to identify a M by K first source matrix, and a second source identifier to identify a K by N second source matrix, each of the matrices containing doubleword elements, and execution circuitry to execute the decoded instruction to perform a flow K times for each element (m, n) of the specified destination matrix to generate eight products by multiplying each nibble of a doubleword element (M,K) of the specified first source matrix by a corresponding nibble of a doubleword element (K,N) of the specified second source matrix, and to accumulate and saturate the eight products with previous contents of the doubleword element.

A computing unit is disclosed, comprising a first memory bank for storing input activations and a second memory bank for storing parameters used in performing computations. The computing unit includes at least one cell comprising at least one multiply accumulate (“MAC”) operator that receives parameters from the second memory bank and performs computations. The computing unit further includes a first traversal unit that provides a control signal to the first memory bank to cause an input activation to be provided to a data bus accessible by the MAC operator. The computing unit performs one or more computations associated with at least one element of a data array, the one or more computations being performed by the MAC operator and comprising, in part, a multiply operation of the input activation received from the data bus and a parameter received from the second memory bank.

Systems and methods for searching data are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a sample dataset and identifying a data schema of the sample dataset. The operations may include generating a sample data vector that includes statistical metrics of the sample dataset and information based on the data schema of the sample dataset. The operations may include searching a data index comprising a plurality of stored data vectors corresponding to a plurality of reference datasets. The stored data vectors may include statistical metrics of the reference datasets and information based on corresponding data schema. The operations may include generating, based on the search and the sample data vector, one or more similarity metrics of the sample dataset to individual ones of the reference datasets.

Systems, devices, and methods related to a deep learning accelerator and memory are described. For example, a removable media (e.g., a memory card, or a USB drive) may be configured to execute instructions with matrix operands and configured with: an interface to receive a video stream; and random access memory to buffer a portion of the video stream as an input to an artificial neural network and to store instructions executable by the deep learning accelerator and matrices of the artificial neural network. Such a removable media can be used to replace an existing removable media used in a surveillance camera to record video or images. The deep learning accelerator can execute the instructions to generate analytics of the buffer portion using the artificial neural network, enabling the surveillance camera that is upgraded via the use of the removable media to provide intelligent services based on the analytics.

An apparatus and method for multiplying packed real and imaginary components of complex numbers and complex conjugates. For example, one embodiment of a processor comprises: a decoder to decode a first instruction to generate a decoded instruction; a first source register to store a first plurality of packed real and imaginary data elements; a second source register to store a second plurality of packed real and imaginary data elements; and execution circuitry to execute the decoded instruction. The execution circuitry includes multiplier circuitry to multiply select real and imaginary data elements in the first and second source registers to generate a plurality of real and imaginary products; adder circuitry to add/subtract various real and imaginary products, scale the results according to an immediate of the instruction, round the scaled results; and saturation circuitry to saturate the rounded results.