Microsoft Technology Licensing, LLC

Applied Micro Circuits Corporation

Jinwen Xi

Marc Tremblay

Bharadwaj Pudipeddi

Maral Mesmakhosroshahi

Eric S Chung

Ming Gang Liu

Saurabh Mohan Kulkarni

Sujeeth Subramanya Bharadwaj

Nuno Claudino Pereira Lopes

Matthias Baenninger

Douglas Christopher Burger

Maged F Barsoum

Dariush Dabiri

Bita Darvish Rouhani

Venmugil Elango

Rasoul Shafipour

Jeremy Fowers

Devangkumar Patel

Systems and methods related to hardware-assisted gradient optimization using streamed gradients are described. An example method in a system comprising a memory configured to store weights associated with a neural network model comprising L layers, where L is an integer greater than one, a gradient optimizer, and a plurality of workers is described. The method includes during a single burst cycle moving a first set of gradients, received from each of the plurality of workers, from at least one gradient buffer to the gradient optimizer and moving weights from at least one buffer, coupled to the memory, to the gradient optimizer. The method further includes during the single burst cycle writing back the new weights, calculated by the gradient optimizer, to the memory. The method further includes during the single burst cycle transmitting the new weights, from the gradient optimizer, to each of the plurality of workers.

Hardware-assisted gradient optimization using streamed gradients

A field programmable gate array (FPGA) including a configurable interconnect fabric connecting a plurality of logic blocks, the configurable interconnect fabric and the logic blocks being configured to implement a data masking circuit configured to: receive input data including data values at a plurality of indices of the input data; select between a data value of the data values and an alternative value using a masking multiplexer to generate masked data, the masking multiplexer being controlled by a mask value of a plurality of mask values at indices corresponding to the indices of the input data; and output the masked data. In some examples, the configurable interconnect fabric and the logic blocks are further configured to implement a mask generation circuit configured to generate the mask values. In some examples, the mask values are received from external memory.

Systems and methods for hardware acceleration of data masking

Systems and methods for hardware acceleration of data masking using a field programmable gate array

Embodiments of the present disclosure include systems and methods for providing hierarchical and shared exponent floating point data types. First and second shared exponent values are determined based on exponent values of a plurality of floating point values. A third shared exponent value is determined based the first shared exponent value and the second shared exponent value. First and second difference values are determined based on the first shared exponent value, the second shared exponent value, and the third shared exponent value. Sign values and mantissa values are determined for the plurality of floating point values. The sign value and the mantissa value for each floating point value in the plurality of floating point values, the third shared exponent value, the first difference value, and the second difference value are stored in a data structure for a shared exponent floating point data type.

Hierarchical and shared exponent floating point data types

Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

Systems and methods for error recovery

Systems and methods related to dual-momentum gradient optimization with reduced memory requirements are described. An example method in a system comprising a gradient optimizer and a memory configured to store momentum values associated with a neural network model comprising L layers is described. The method includes retrieving from the memory a first set of momentum values and a second set of momentum values, corresponding to a layer of the neural network model, having a selected storage format. The method further includes converting the first set of momentum values to a third set of momentum values having a training format associated with the gradient optimizer and converting the second set of momentum values to a fourth set of momentum values having a training format associated with the gradient optimizer. The method further includes performing gradient optimization using the third set of momentum values and the fourth set of momentum values.

Dual-momentum gradient optimization with reduced memory requirements

Systems, methods, and apparatuses are provided for compressing values. A plurality of parameters may be obtained from a memory, each parameter comprising a floating-point number that is used in a relationship between artificial neurons or nodes in a model. A mantissa value and an exponent value may be extracted from each floating-point number to generate a set of mantissa values and a set of exponent values. The set of mantissa values may be compressed to generate a mantissa lookup table (LUT) and a plurality of mantissa LUT index values. The set of exponent values may be encoded to generate an exponent LUT and a plurality of exponent LUT index values. The mantissa LUT, mantissa LUT index values, exponent LUT, and exponent LUT index values may be provided to one or more processing entities to train the model.

Lossless exponent and lossy mantissa weight compression for training deep neural networks

Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. To improve efficiency, the input samples may be divided into microbatches, and a plurality of microbatches executing in sequential order may form a minibatch. The size of the group of microbatches or minibatch can be adjusted to reduce the communication overhead. Multi-level parallel parameters reduction may be performed at the parameter server and the target device.

Data parallelism in distributed training of artificial intelligence models

Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. This paradigm of executing one portion of the AI model at a time allows for dynamic execution of the large AI model.

Dynamic multi-layer execution for artificial intelligence modeling

Methods and systems for multi-input IIR filters with error feedback are disclosed. By using multiple-inputs to generate multiple outputs during each iteration, a multi-input IIR filter in accordance with the present invention has greatly increased throughput. Furthermore, the addition of a multi-variable error feedback unit in accordance with the present invention in a multiple-input IIR filter can greatly increase the accuracy of the multi-variable IIR Filter.

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Sunnyvale, CA, United States of America

Jinwen Xi