Amazon Technologies, Inc.

Jiading Gai

Randy Renfu Huang

Vignesh Vivekraja

Hongbin Zheng

Thiam Khean Hah

Animesh Jain

Techniques for implementing bidirectional networks, such as bidirectional recurrent neural networks (RNNs), on data-flow centric processors are disclosed. An input to a bidirectional network is provided. The bidirectional network includes a forward network and a reverse network. The forward network performs a set of operations in a forward direction and the reverse network performs the set of operations in a reverse direction. A forward output is generated using the forward network based on the input. A first transformation is performed on the input to generate a transformed input. A reverse output is generated using the reverse network based on the transformed input. A second transformation is performed on the reverse output to generate a transformed output. The forward output and the transformed output are combined to produce an overall output.

Bidirectional network on a data-flow centric processor

A single instruction multiple data (SIMD) processor is used to implement a dropout layer between a first layer and a second layer of a neural network. The SIMD processor can implement the dropout layer by setting one or more elements in an output tensor of the first layer to zero before providing it as an input tensor to the second layer. Setting of the one or more elements to zero is based on a dropout rate, and pseudo-random numbers generated by a random number generator in the SIMD processor.

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Seattle, WA, United States of America

Jiading Gai