Abstract: Embodiments described herein include, software, firmware, and hardware logic that provides techniques to perform arithmetic on sparse data via a systolic processing unit. Embodiment described herein provided techniques to detect zero value elements within a vector or a set of packed data elements output by a processing resource and generate metadata to indicate a location of the zero value elements within the plurality of data elements.

Abstract: The disclosed circuit is configured to round a value in a first number format using a random value. Using the rounded value, the circuit can convert the rounded value to a second number format that has a lower precision than a precision of the first number format. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed circuit can select micro-operations specifically for converting a value in a first number format to a second number format. The circuit can include micro-operations for various conversions between different number formats, including number formats of different floating-point precisions. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed circuit can interpret a bit sequence as a value based on one of multiple floating point number formats in a bias mode indicated by a bias mode indicator. The circuit can and perform an operation using the value in the bias mode. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed processing circuit can perform an operation with a first operand having a first number format and a second operand having a second number format by directly using the first operand in the first number format and the second operand in the second number format to produce an output result. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Embodiments described herein include software, firmware, and hardware logic that provides techniques to perform arithmetic on sparse data via a systolic processing unit. One embodiment provides for data aware sparsity via compressed bitstreams. One embodiment provides for block sparse dot product instructions. One embodiment provides for a depth-wise adapter for a systolic array.

Abstract: Embodiments described herein include software, firmware, and hardware logic that provides techniques to perform arithmetic on sparse data via a systolic processing unit. One embodiment provides for data aware sparsity via compressed bitstreams. One embodiment provides for block sparse dot product instructions. One embodiment provides for a depth-wise adapter for a systolic array.

Abstract: Described herein is a graphics processor including a plurality of processing clusters coupled with a host interface, each processing cluster comprising a plurality of multiprocessors, the plurality of multiprocessors interconnected via a data interconnect, and each multiprocessor comprising sparse matrix multiply acceleration hardware including a systolic processing array with feedback inputs.

Abstract: A system and method relate to excluding irrelevant data from talent profiles, including obtaining a first talent profile, wherein the first talent profile comprises an identifier of a person, and a plurality of key-value pairs characterizing aspects of the person, determining whether one or more keys in the plurality of key-value pairs are relevant to a job role, generating a second talent profile by for each of the one or more keys that is determined irrelevant to the job role, excluding the corresponding key-value pair from the first talent profile, for each of the one or more keys that is determined relevant to the job role, determining an abstracted value that is relevant to the job role and encompasses the value, and replacing the value in the corresponding key-value pair with the abstracted value in the first talent profile, and presenting the second talent profile to a profile reviewer.

Abstract: A method and system for predicting a match between a candidate and a job position for an organization include generating an enriched talent profile associated with the candidate, the enriched talent profile comprising first characteristic values and second characteristic values, wherein the first characteristic values relate to characteristics verifiably possessed by the candidate, and second characteristic values are predicted values by executing a first neural network module, generating a calibrated job profile for the job position, the calibrated job profile comprising job requirements for the job position, and executing a second neural network module using the enriched talent profile and the calibrated job profile as inputs to calculate one or more hire-related prediction values for the candidate.

Abstract: Graphics processors and graphics processing units having dot product accumulate instructions for a hybrid floating point format are disclosed. In one embodiment, a graphics multiprocessor comprises an instruction unit to dispatch instructions and a processing resource coupled to the instruction unit. The processing resource is configured to receive a dot product accumulate instruction from the instruction unit and to process the dot product accumulate instruction using a bfloat16 number (BF16) format.

Abstract: An apparatus to facilitate utilizing structured sparsity in systolic arrays is disclosed. The apparatus includes a processor comprising a systolic array to receive data from a plurality of source registers, the data comprising unpacked source data, structured source data that is packed based on sparsity, and metadata corresponding to the structured source data; identify portions of the unpacked source data to multiply with the structured source data, the portions of the unpacked source data identified based on the metadata; and output, to a destination register, a result of multiplication of the portions of the unpacked source data and the structured source data.

Abstract: A system and method relate to excluding biasing data from talent profiles, including identifying one or more classes of bias, obtaining a first talent profile, wherein the first talent profile comprises an identifier of a person, and a plurality of values characterizing aspects of the person, determining, from the plurality of values, a first value that is indicative of influences over at least one of the one or more classes of bias, and removing or substituting the first value in the first talent profile to generate a second talent profile.

Abstract: Described herein is a graphics processor including a plurality of processing clusters coupled with a host interface, each processing cluster comprising a plurality of multiprocessors, the plurality of multiprocessors interconnected via a data interconnect, and each multiprocessor comprising sparse matrix multiply acceleration hardware including a systolic processing array with feedback inputs.

Abstract: An apparatus to facilitate acceleration of matrix multiplication operations. The apparatus comprises a systolic array including matrix multiplication hardware to perform multiply-add operations on received matrix data comprising data from a plurality of input matrices and sparse matrix acceleration hardware to detect zero values in the matrix data and perform one or more optimizations on the matrix data to reduce multiply-add operations to be performed by the matrix multiplication hardware.

Abstract: Graphics processors and graphics processing units having dot product accumulate instructions for a hybrid floating point format are disclosed. In one embodiment, a graphics multiprocessor comprises an instruction unit to dispatch instructions and a processing resource coupled to the instruction unit. The processing resource is configured to receive a dot product accumulate instruction from the instruction unit and to process the dot product accumulate instruction using a bfloat16 number (BF16) format.

Abstract: An audio track capturing a conversation between an interviewer and an interviewee during an interview may be segmented, by executing a speaker identification engine, into a plurality of audio segments each being tagged as associated with one of the interviewer or the interviewee. A speech recognition and natural language processing (NLP) engine may be applied to the audio track to determine attributes associated with audio segments being tagged to the interviewer. The attributes may comprise timing parameters associated with the audio segment and a text content of the audio segment. A rule-based analysis engine may be executed, based on the attributes associated with audio segments being tagged to the interviewer, to determine whether the interviewer conducts the interview in compliance with predetermined rules. Responsive to determining that the interviewer does not conduct the interview in compliance with the predetermined rules, generating a notice regarding the non-compliance of the interview.

Abstract: Embodiments described herein include software, firmware, and hardware logic that provides techniques to perform arithmetic on sparse data via a systolic processing unit. One embodiment provides for data aware sparsity via compressed bitstreams. One embodiment provides for block sparse dot product instructions. One embodiment provides for a depth-wise adapter for a systolic array.

Abstract: An apparatus to facilitate utilizing structured sparsity in systolic arrays is disclosed. The apparatus includes a processor comprising a systolic array to receive data from a plurality of source registers, the data comprising unpacked source data, structured source data that is packed based on sparsity, and metadata corresponding to the structured source data; identify portions of the unpacked source data to multiply with the structured source data, the portions of the unpacked source data identified based on the metadata; and output, to a destination register, a result of multiplication of the portions of the unpacked source data and the structured source data.

Abstract: Described herein is a graphics processing unit (GPU) configured to receive an instruction having multiple operands, where the instruction is a single instruction multiple data (SIMD) instruction configured to use a bfloat16 (BF16) number format and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent. The GPU can process the instruction using the multiple operands, where to process the instruction includes to perform a multiply operation, perform an addition to a result of the multiply operation, and apply a rectified linear unit function to a result of the addition.