Intel Corporation

Sudarshan Srinivasan

Dipankar Das

Sasikanth Avancha

Subramaniam M Maiyuran

Varghese George

Guei-Yuan Lueh

Jorge E Parra

Wei-Yu Chen

Chandra S Gurram

Supratim Pal

Ashutosh Garg

Ishwar Bhati

Junjie Gu

Martin Langhammer

Gregg William Baeckler

Aravindh V Anantaraman

Srinivas Sridharan

Mohammad R Haghighat

Ajaya V Durg

Mikhail E Smorkalov

Duncan Moss

Embodiments described herein provide for an instruction and associated logic to enable a vector multiply add instructions with automatic zero skipping for sparse input. One embodiment provides for a general-purpose graphics processor comprising logic to perform operations comprising fetching a hardware macro instruction having a predicate mask, a repeat count, and a set of initial operands, where the initial operands include a destination operand and multiple source operands. The hardware macro instruction is configured to perform one or more multiply/add operations on input data associated with a set of matrices.

Instructions and logic for vector multiply add with zero skipping

The present disclosure relates generally to techniques for improving the implementation of certain operations on an integrated circuit. In particular, deep learning techniques, which may use a deep neural network (DNN) topology, may be implemented more efficiently using low-precision weights and activation values by efficiently performing down conversion of data to a lower precision and by preventing data overflow during suitable computations. Further, by more efficiently mapping multipliers to programmable logic on the integrated circuit device, the resources used by the DNN topology to perform, for example, inference tasks may be reduced, resulting in improved integrated circuit operating speeds.

Circuitry for low-precision deep learning

Systems, apparatuses and methods may provide for technology that detects a runtime call to a communication library, wherein the runtime call identifies a memory buffer, determines that a class of service (CLOS) attribute is associated with the memory buffer, and issues a driver instruction to modify the CLOS attribute in response to the runtime call.

Initialization and management of class of service attributes in runtime to optimize deep learning training in distributed environments

Growing community of inventors

Bengaluru, India

Sudarshan Srinivasan