Advanced Micro Devices Corporation

Ati Technologies Ulc

Silicon Graphics, Incorporated

Ati Technologies Inc.

Microsoft Technology Licensing, LLC

Rpx Corporation

Advanced Silicon Technologies LLC

Greg Sadowski

John Kalamatianos

Steven E Raasch

David Andrew Roberts

Shomit N Das

Stephen David Presant

Wayne Burleson

John A Klenoski

John Wakefield Brothers

George Jacobs

Alexander J Branover

Maurice B Steinman

David I J Glen

Paul Chow

David L Dignam

Daniel Wong

Ying Chen

Thomas E Ryan

Joseph P Kennedy

Konstantine I Iourcha

Gabor Sines

Warren Fritz Kruger

Michael J Schulte

Philip J Rogers

Nathaniel David Naegle

Edward J McLellan

Haibo Liu

Aris Balatsos

Gregory M Eitzmann

Kevin O'Neil

Wai Ki Lo

Sriram Sundaram

David Ij Glen

Stephen Kushnir

Syed Zohaib M Gilani

William C Brantley

Stephen Edward Smith

Tri Minh Nguyen

Eric Kunze

Bob Williams

Weiwei Jiang

John D Hallesy

An apparatus includes a plurality of registers to store sets of state information that represent a state history of a processing unit. The apparatus also includes a power management advisor (PMA) to generate a signal based on the sets of state information, wherein the signal indicates a probability that a power state transition of the processing unit achieves a target outcome. In some cases, the signal is provided to a power management controller including hardware circuitry that initiates a power state transition of the processing unit based on the signal and inputs to the power management controller that represent a subset of the state information corresponding to a current power state of the processing unit.

Power management advisor to support power management control

Systems, apparatuses, and methods for managing power consumption for a neural network implemented on multiple graphics processing units (GPUs) are disclosed. A computing system includes a plurality of GPUs implementing a neural network. In one implementation, the plurality of GPUs draw power from a common power supply. To prevent the power consumption of the system from exceeding a power limit for long durations, the GPUs coordinate the scheduling of tasks of the neural network. At least one or more first GPUs schedule their computation tasks so as not to overlap with the computation tasks of one or more second GPUs. In this way, the system spends less time consuming power in excess of a power limit, allowing the neural network to be implemented in a more power efficient manner.

Neural network power management in a multi-GPU system

Exemplary embodiments provide wear spreading among die regions (i.e., one or more circuits) in an integrated circuit or among dies by using operating condition data in addition to or instead of environmental data such as temperature data, from each of a plurality of die regions. Control logic produces a cumulative amount of time each of the plurality of die regions has spent at an operating condition based on operating condition data wherein the operating condition data is based on at least one of the following operating characteristics: frequency of operation of the plurality of die regions, an operating voltage of the plurality of die regions, an activity level of the plurality of die regions, a timing margin of the plurality of die regions, and a number of detected faults of the plurality of die regions. The method and apparatus spreads wear among the plurality of same type of die regions by controlling task execution among the plurality of die regions using the die wear-out data.

Method and apparatus for providing wear leveling

Exemplary embodiments provide thermal wear spreading among a plurality of thermal die regions in an integrated circuit or among dies by using die region wear-out data that represents a cumulative amount of time each of a number of thermal die regions in one or more dies has spent at a particular temperature level. In one example, die region wear-out data is stored in persistent memory and is accrued over a life of each respective thermal region so that a long term monitoring of temperature levels in the various die regions is used to spread thermal wear among the thermal die regions. In one example, spreading thermal wear is done by controlling task execution such as thread execution among one or more processing cores, dies and/or data access operations for a memory.

Method and apparatus for providing thermal wear leveling

An asynchronous pipeline includes a first stage and one or more second stages. A controller provides control signals to the first stage to indicate a modification to an operating speed of the first stage. The modification is determined based on a comparison of a completion status of the first stage to one or more completion statuses of the one or more second stages. In some cases, the controller provides control signals indicating modifications to an operating voltage applied to the first stage and a drive strength of a buffer in the first stage. Modules can be used to determine the completion statuses of the first stage and the one or more second stages based on the monitored output signals generated by the stages, output signals from replica critical paths associated with the stages, or a lookup table that indicates estimated completion times.

Controlling the operating speed of stages of an asynchronous pipeline

One or more processors are operative to carry out neural network operations and include a plurality of compute units (CUs) configurable for neural network operations. The neural network compute unit remapping logic detects a condition to remap neural network compute units that are currently used in carrying out a neural network operation in a processor with at least one replacement compute unit that is not currently being used to carry out the neural network operation. In response to detecting the condition, the logic remaps a logical address of at least one currently used compute unit to a different physical address that corresponds to the replacement compute unit and causes the replacement compute unit to carry out neural network operations.

Machine intelligence processor with compute unit remapping

A multi-die semiconductor package includes a first integrated circuit (IC) die having a first intrinsic performance level and a second IC die having a second intrinsic performance level different from the first intrinsic performance level. A power management controller distributes, based on a determined die performance differential between the first IC die and the second IC die, a level of power allocated to the semiconductor chip package between the first IC die and the second IC die. In this manner, the first IC die receives and operates at a first level of power resulting in performance exceeding its intrinsic performance level. The second IC die receives and operates at a second level of power resulting in performance below its intrinsic performance level, thereby reducing performance differentials between the IC dies.

Multi-die system performance optimization

Various energy efficient data encoding schemes and computing devices are disclosed. In one aspect, a method of transmitting data from a transmitter to a receiver connected by plural wires is provided. The method includes sending from the transmitter on at least one but not all of the wires a first wave form that has first and second signal transitions. The receiver receives the first waveform and measures a first duration between the first and second signal transitions using a locally generated clock signal not received from the transmitter. The first duration is indicative of a first particular data value.

Energy efficient adaptive data encoding method and circuit

A processing unit includes a plurality of components configured to execute instructions and a controller. The controller is configured to determine a power consumption of the processing unit, determine a waiting status of the processing unit based on waiting statuses of components, and selectively modify an operating state of the processing unit based on the waiting status and the power consumption of the processing unit. In some cases, the operating state is modified in response to a percentage of the components that are waiting for an action to complete being below a threshold percentage and the power consumption of the processing unit being below a power limit. In some cases, the controller identifies a pattern in the power consumption by the processing unit and modifies the operating state of the processing unit to increase the power consumption of the processing unit based on the pattern identified by the controller.

Modifying an operating state of a processing unit based on waiting statuses of blocks

Systems, apparatuses, and methods for optimizing neural network training with a first-in, last-out (FILO) buffer are disclosed. A processor executes a training run of a neural network implementation by performing multiple passes and adjusting weights of the neural network layers on each pass. Each training phase includes a forward pass and a backward pass. During the forward pass, each layer, in order from first layer to last layer, stores its weights in the FILO buffer. An error is calculated for the neural network at the end of the forward pass. Then, during the backward pass, each layer, in order from last layer to first layer, retrieves the corresponding weights from the FILO buffer. Gradients are calculated based on the error so as to update the weights of the layer for the next pass through the neural network.

Neural network internal data fast access memory buffer

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Cambridge, MA, United States of America

Greg Sadowski