Advanced Micro Devices Corporation

Mayank Daga

Mauricio Breternitz, Jr

Joseph Lee Greathouse

Daniel I Lowell

Sergey Voronov

Methods, devices, systems, and instructions for adaptive quantization in an artificial neural network (ANN) calculate a distribution of ANN information; select a quantization function from a set of quantization functions based on the distribution; apply the quantization function to the ANN information to generate quantized ANN information; load the quantized ANN information into the ANN; and generate an output based on the quantized ANN information. Some examples recalculate the distribution of ANN information and reselect the quantization function from the set of quantization functions based on the resampled distribution if the output does not sufficiently correlate with a known correct output. In some examples, the ANN information includes a set of training data. In some examples, the ANN information includes a plurality of link weights.

Adaptive quantization for neural networks

Systems, apparatuses, and methods for adjusting group sizes to match a processor lane width are described. In early iterations of an algorithm, a processor partitions a dataset into groups of data points which are integer multiples of the processing lane width of the processor. For example, when performing a K-means clustering algorithm, the processor determines that a first plurality of data points belong to a first group during a given iteration. If the first plurality of data points is not an integer multiple of the number of processing lanes, then the processor reassigns a first number of data points from the first plurality of data points to one or more other groups. The processor then performs the next iteration with these first number of data points assigned to other groups even though the first number of data points actually meets the algorithmic criteria for belonging to the first group.

System and method for parallelization of data processing in a processor

A method and apparatus for performing a top-down Breadth-First Search (BFS) includes performing a first determination whether to convert to a bottom-up BFS. A second determination is performed whether to convert to the bottom-up BFS, based upon the first determination being positive. The bottom-up BFS is performed, based upon the first determination and the second determination being positive. A third determination is made whether to convert from the bottom-up BFS to the top-down BFS, based upon the third determination being positive.

Method and apparatus for performing a search operation on heterogeneous computing systems

A method of multiplication of a sparse matrix and a vector to obtain a new vector and a system for implementing the method are claimed. Embodiments of the method are intended to optimize the performance of sparse matrix-vector multiplication in highly parallel processors, such as GPUs. The sparse matrix is stored in compressed sparse row (CSR) format.

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Mayank Daga