Abstract: An apparatus and method for performing efficient, adaptable tensor operations. For example, one embodiment of a processor comprises: front end circuitry to schedule matrix operations responsive to a matrix multiplication instruction; a plurality of lanes to perform parallel execution of the matrix operations, wherein a lane comprises an arithmetic logic unit to multiply a block of a first matrix with a block of a second matrix to generate a product and to accumulate the product with a block of a third matrix, and wherein the matrix blocks are to be stored in registers within the lane; and broadcast circuitry to broadcast one or more invariant matrix blocks to at least one of different registers within the lane and different registers across different lanes.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for composable machine learning compute nodes. An example apparatus includes interface circuitry to receive a workload, instructions in the apparatus, and processor circuitry to at least one of execute or instantiate the instructions to generate a first configuration of one or more machine-learning models based on a workload, generate a second configuration of hardware, determine an evaluation parameter based on an execution of the workload, the execution of the workload based on the first configuration and the second configuration, and, in response to the evaluation parameter satisfying a threshold, execute the one or more machine-learning models in the first configuration on the hardware in the second configuration, the one or more machine-learning models and the hardware to execute the workload.

Abstract: Examples described herein relate to offload circuitry comprising one or more compute engines that are configurable to perform a workload offloaded from a process executed by a processor based on a descriptor particular to the workload. In some examples, the offload circuitry is configurable to perform the workload, among multiple different workloads. In some examples, the multiple different workloads include one or more of: data transformation (DT) for data format conversion, Locality Sensitive Hashing (LSH) for neural network (NN), similarity search, sparse general matrix-matrix multiplication (SpGEMM) acceleration of hash based sparse matrix multiplication, data encode, data decode, or embedding lookup.

Abstract: Methods, apparatus, systems, and articles of manufacture for data enhanced automated model generation are disclosed. Example instructions, when executed, cause at least one processor to access a request to generate a machine learning model to perform a selected task, generate task knowledge based on a previously generated machine learning model, create a search space based on the task knowledge, and generate a machine learning model using neural architecture search, the neural architecture search beginning based on the search space.

Abstract: In one embodiment, a cache memory includes: a plurality of data banks, each of the plurality of data banks having a plurality of entries each to store a portion of a cache line distributed across the plurality of data banks; and a plurality of tag banks decoupled from the plurality of data banks, wherein a tag for a cache line is to be assigned to one of the plurality of tag banks. Other embodiments are described and claimed.

Abstract: An apparatus and method for performing efficient, adaptable tensor operations. For example, one embodiment of a processor comprises: front end circuitry to schedule matrix operations responsive to a matrix multiplication instruction; a plurality of lanes to perform parallel execution of the matrix operations, wherein a lane comprises an arithmetic logic unit to multiply a block of a first matrix with a block of a second matrix to generate a product and to accumulate the product with a block of a third matrix, and wherein the matrix blocks are to be stored in registers within the lane; and broadcast circuitry to broadcast one or more invariant matrix blocks to at least one of different registers within the lane and different registers across different lanes.

Abstract: An apparatus and method for data parallel single program multiple data (SPMD) execution. For example, one embodiment of a processor comprises: instruction fetch circuitry to fetch instructions of one or more primary threads; a decoder to decode the instructions to generate uops; a data parallel cluster (DPC) to execute microthreads comprising a subset of the uops, the DPC further comprising: a plurality of execution lanes to perform parallel execution of the microthreads; an instruction decode queue (IDQ) to store the uops prior to execution; and a scheduler to evaluate the microthreads based on associated variables including instruction pointer (IP) values, the scheduler to gang microthreads into fragments for parallel execution on the execution lanes based on the evaluation.

Abstract: In one embodiment, a cache memory includes: a plurality of data banks, each of the plurality of data banks having a plurality of entries each to store a portion of a cache line distributed across the plurality of data banks; and a plurality of tag banks decoupled from the plurality of data banks, wherein a tag for a cache line is to be assigned to one of the plurality of tag banks. Other embodiments are described and claimed.

Abstract: An apparatus and method for performing efficient, adaptable tensor operations.

Abstract: An apparatus and method for performing efficient, adaptable tensor operations.

Abstract: An apparatus and method for data parallel single program multiple data (SPMD) execution. For example, one embodiment of a processor comprises: instruction fetch circuitry to fetch instructions of one or more primary threads; a decoder to decode the instructions to generate uops; a data parallel cluster (DPC) to execute microthreads comprising a subset of the uops, the DPC further comprising: a plurality of execution lanes to perform parallel execution of the microthreads; an instruction decode queue (IDQ) to store the uops prior to execution; and a scheduler to evaluate the microthreads based on associated variables including instruction pointer (IP) values, the scheduler to gang microthreads into fragments for parallel execution on the execution lanes based on the evaluation.

Abstract: An apparatus and method is described herein for providing a test, validation, and debug architecture. At a target or base level, hardware hooks (Design for Test or DFx) are designed into and integrated with silicon parts. A controller may provide abstracted access to such hooks, such as through an abstraction layer that abstracts low level details of the hardware DFx. In addition, the abstraction layer through an interface, such as APIs, provides services, routines, and data structures to higher-level software/presentation layers, which are able to collect test data for validation and debug of a unit/platform under test. Moreover, the architecture potentially provides tiered (multiple levels of) secure access to the test architecture. Additionally, physical access to the test architecture for a platform may be simplified through use of a unified, bi-directional test access port, while also potentially allowing remote access to perform remote test and debug of a part/platform under test.

Abstract: An apparatus and method is described herein for providing a test, validation, and debug architecture. At a target or base level, hardware (Design for Test or DFx) are designed into and integrated with silicon parts. A controller may provide abstracted access to such hooks, such as through an abstraction layer that abstracts low level details of the hardware DFx. In addition, the abstraction layer through an interface, such as APIs, provides services, routines, and data structures to higher-level software/presentation layers, which are able to collect test data for validation and debug of a unit/platform under test. Moreover, the architecture potentially provides tiered (multiple levels of) secure access to the test architecture. Additionally, physical access to the test architecture for a platform may be simplified through use of a unified, bi-directional test access port, while also potentially allowing remote access to perform remote test and de-bug of a part/platform under test.