Abstract:
One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute a 32-bit intermediate product of 16-bit operands and to compute a 32-bit sum based on the 32-bit intermediate product.
Abstract:
A processing apparatus is provided comprising a multiprocessor having a multithreaded architecture. The multiprocessor can execute at least one single instruction to perform parallel mixed precision matrix operations. In one embodiment the apparatus includes a memory interface and an array of multiprocessors coupled to the memory interface. At least one multiprocessor in the array of multiprocessors is configured to execute a fused multiply-add instruction in parallel across multiple threads.
Abstract:
Disclosed embodiments relate to a variable format, variable sparsity matrix multiplication (VFVSMM) instruction. In one example, a processor includes fetch and decode circuitry to fetch and decode a VFVSMM instruction specifying locations of A, B, and C matrices having (M×K), (K×N), and (M×N) elements, respectively, execution circuitry, responsive to the decoded VFVSMM instruction, to: route each row of the specified A matrix, staggering subsequent rows, into corresponding rows of a (M×N) processing array, and route each column of the specified B matrix, staggering subsequent columns, into corresponding columns of the processing array, wherein each of the processing units is to generate K products of A-matrix elements and matching B-matrix elements having a same row address as a column address of the A-matrix element, and to accumulate each generated product with a corresponding C-matrix element.
Abstract:
One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute a 32-bit intermediate product of 16-bit operands and to compute a 32-bit sum based on the 32-bit intermediate product.
Abstract:
An instruction and logic for a Simon-based hashing for validation are described. In one embodiment, a processor comprises: a memory the memory to store a plurality of values; and a hash circuit comprising a Simon cipher circuit operable to receive the plurality of values from the memory, to apply a Simon cipher, and to generate an output for each of the plurality of values; and circuitry coupled to the Simon cipher circuit to combine outputs from the Simon cipher circuit for each value of the plurality of values into a hash digest that is indicative of whether the values in the memory are valid.
Abstract:
A packet-switched request from a first router of a network-on-chip is received. The packet-switched request is generated by source logic of the network-on-chip. Circuit-switched data associated with the packet switched request is also received. The circuit-switched data is stored by a storage element. The circuit-switched data is sent towards destination logic identified in the packet-switched request.
Abstract:
A first packet-switched reservation request is received. Data associated with the first packet-switched reservation request is communicated through a first circuit-switched channel according to a best effort communication scheme. A second packet-switched reservation request is received. Data associated with the second packet-switched reservation request is communicated through a second circuit-switched channel according to a guaranteed throughput communication scheme.
Abstract:
A multicast message that is to originate from a source is received. The multicast message comprises an identifier. A plurality of directions in which the multicast message is to fork at the router are stored. A plurality of messages from the directions in which the multicast message is to fork are received. The received messages are to comprise the identifier. The plurality of messages are aggregated into an aggregate message and sent towards the source.
Abstract:
In an embodiment, a router includes multiple input ports and output ports, where the router is of a source-synchronous hybrid network on chip (NoC) to enable communication between routers of the NoC based on transitions in control flow signals communicated between the routers. Other embodiments are described and claimed.
Abstract:
An apparatus may comprise a plurality of ports and a plurality of channel reservation banks A channel reservation bank is to be associated with a port of the plurality of ports. The channel reservation bank is to comprise a plurality of channel reservation slots. The port of the plurality of ports is to comprise a plurality of circuit-switched channels through the port. The configuration of each of the plurality of circuit-switched channels to be based on information stored in a channel reservation slot of the channel reservation bank to be associated with the port.