Abstract:
Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.
Abstract:
A special-purpose processing system, a method of carrying out sharing special-purpose processing resources and a graphics processing system. In one embodiment, the special-purpose processing system includes: (1) a special-purpose processing resource and (2) a Representational State Transfer (ReST) application programming interface operable to process data using the special-purpose processing resource in response to stateless commands based on a standard protocol selected from the group consisting of: (2a) a standard network protocol and (2b) a standard database query protocol.
Abstract:
Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.
Abstract:
Aspects of the present invention are directed to computer-implemented techniques for improving the training of artificial neural networks using a reduced precision (e.g., float16) data format. Embodiments of the present invention rescale tensor values prior to performing matrix operations (such as matrix multiplication or matrix addition) to prevent overflow and underflow. To preserve accuracy throughout the performance of the matrix operations, the scale factors are defined using a novel data format to represent tensors, wherein a matrix is represented by the tuple X, where X=(a, v[.]), wherein a is a float scale factor and v[.] are scaled values stored in the float16 format. The value of any element X[i] according to this data format would be equal to a*v[i].
Abstract:
A device comprises one or more circuits that dynamically adjust a load profile of one or more processing devices processing a workload in a bulk-synchronous mode.
Abstract:
A special-purpose processing system, a method of carrying out sharing special-purpose processing resources and a graphics processing system. In one embodiment, the special-purpose processing system includes: (1) a special-purpose processing resource and (2) a Representational State Transfer (ReST) application programming interface operable to process data using the special-purpose processing resource in response to stateless commands based on a standard protocol selected from the group consisting of: (2a) a standard network protocol and (2b) a standard database query protocol.