Abstract: An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

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 to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

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 performing efficient, adaptable tensor operations.

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

Abstract: Disclosed embodiments relate to apparatuses, systems, and methods for performing sort indexing and/or permutation using an index. An exemplary apparatus includes decode circuitry to decode an instruction, the instruction to include a first field to identify a location of a source vector, a second field to identify a location of a destination vector, and an opcode to indicate to execution circuitry to execute the decoded instruction to sort values of the source vector and store a result of the sort in the destination vector by generating, per each element of the source vector, an index value using one or more comparisons of the element itself and to other data elements of the source vector, and permuting the values of the elements of the source vector based upon the index values for the elements and execution circuitry to execute the decoded instruction as indicated by the opcode.

Abstract: An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

Abstract: The present invention provides a mobile device cradle for a vehicle. The cradle may include docking members for receiving a mobile device of a driver of the vehicle. The cradle may also include a face recognition means for periodically capturing a face of the driver, environmental conditions, and traffic conditions. Further, the cradle may include an authentication unit for authenticating the mobile device and the face of the driver captured by the face recognition means and for providing an authentication signal for starting the vehicle to an electronic engine control unit (EECU). Furthermore, the cradle may include an information collection unit for collecting dynamic information associated with the driver and the vehicle from monitoring units. Moreover, the cradle may also include a communication unit for communicating the dynamic information to back office system(s) communicatively coupled to the mobile device via a wireless communication network.