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: A method can include introducing a device into a lung of a patient to a site of a material lodged in the lung. The device can include a distal end configured to core the material, a proximal end, and a tube that includes a hollow interior. The method can further include coring from the material a piece that is sized to pass through the hollow interior of the tube using the distal end of the device. The method can further include applying suction to the proximal end of the device to pass the piece through the hollow interior of the tube and out of the device through the proximal end. Other and further methods are also disclosed.

Abstract: A device is configured to clear a bolus of food impacted within an esophagus, the device including a catheter tube having a hollow interior and a distal end configured to core the bolus of food and a proximal end configured to be coupled to a source of suction to clear the core.

Abstract: A configurable integrated circuit to compute vector dot products between a first N-bit vector and a second N-bit vector in a plurality of precision modes. An embodiment includes M slices, each of which calculates the vector dot products between a corresponding segment of the first and the second N-bit vectors. Each of the slices outputs intermediary multiplier results for the lower precision modes, but not for highest precision mode. A plurality of adder trees to sum up the plurality of intermediate multiplier results, with each adder tree producing a respective adder out result. An accumulator to merge the adder out result from a first adder tree with the adder out result from a second adder tree to produce the vector dot product of the first and the second N-bit vector in the highest precision mode.

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: One embodiment provides for a machine-learning hardware accelerator comprising a compute unit having an adder and a multiplier that are shared between integer data path and a floating-point datapath, the upper bits of input operands to the multiplier to be gated during floating-point operation.

Abstract: In accordance with some embodiments, the complexity of motion estimation algorithms that use Haar, SAD and Hadamard transforms may be reduced. In some embodiments, the number of summations may be reduced compared to existing techniques and some of the existing summations may be replaced with compare operations. In some embodiments, additions are replaced with compares in order to balance delay and area or energy or power considerations.

Abstract: Systems, apparatuses and methods may provide for technology that conduct a first alignment between a plurality of floating-point numbers based on a first subset of exponent bits. The technology may also conduct, at least partially in parallel with the first alignment, a second alignment between the plurality of floating-point numbers based on a second subset of exponent bits, where the first subset of exponent bits are LSBs and the second subset of exponent bits are MSBs. In one example, technology adds the aligned plurality of floating-point numbers to one another. With regard to the second alignment, the technology may also identify individual exponents of a plurality of floating-point numbers, identify a maximum exponent across the individual exponents, and conduct a subtraction of the individual exponents from the maximum exponent, where the subtraction is conducted from MSB to LSB.

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: 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: Systems, apparatuses, and methods for k-nearest neighbor (KNN) searches are described. In particular, embodiments of a KNN accelerator and its uses are described. In some embodiments, the KNN accelerator includes a plurality of vector partial distance computation circuits each to calculate a partial sum, a minimum sort network to sort partial sums from the plurality of vector partial distance computation circuits to find k nearest neighbor matches and a global control circuit to control aspects of operations of the plurality of vector partial distance computation circuits.

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: A configurable integrated circuit to compute vector dot products between a first N-bit vector and a second N-bit vector in a plurality of precision modes. An embodiment includes M slices, each of which calculates the vector dot products between a corresponding segment of the first and the second N-bit vectors. Each of the slices outputs intermediary multiplier results for the lower precision modes, but not for highest precision mode. A plurality of adder trees to sum up the plurality of intermediate multiplier results, with each adder tree producing a respective adder out result. An accumulator to merge the adder out result from a first adder tree with the adder out result from a second adder tree to produce the vector dot product of the first and the second N-bit vector in the highest precision mode.

Abstract: A system for clearing a blockage from a lumen of a patient, such as an impacted food bolus, can include a catheter that includes a distal end for insertion into the lumen of the patient. The catheter further includes a proximal end for coupling with a suction source at a position external to the patient. The catheter further includes a lumen extending through the proximal and distal ends. The system can include a cutting tip for cutting a morsel from the bolus as suction is applied to the bolus via the lumen of the catheter. The system can also include a positioning element that transitions from an undeployed state to a deployed state, which is expanded relative to the undeployed state. The positioning element can contact the esophagus to space the cutting tip from the esophagus when in the deployed state.

Abstract: One embodiment provides for a machine-learning hardware accelerator comprising a compute unit having an adder and a multiplier that are shared between integer data path and a floating-point datapath, the upper bits of input operands to the multiplier to be gated during floating-point operation.

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 system for clearing a blockage in a patient can include a tubular member that defines a channel, the tubular member being insertable into an esophagus of the patient. The system can further include a catheter assembly that includes a catheter tube, which defines a length greater than a length of the tubular member and is passable through the channel of the tubular member while the tubular member is positioned in the esophagus of the patient. The catheter tube can include a distal tip that defines a cutting element to core the blockage positioned in the esophagus of the patient. The catheter assembly can further include a proximal end coupled with the catheter tube, the proximal end being couplable with a vacuum line such that, when suction is provided via the vacuum line, advancement of the catheter tube into contact with the blockage cores from the blockage a piece of the blockage that is passed through the catheter.

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.