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Recent application requirements show a trend toward higher power requirements for boost converters. These requirements often put great emphasis on cost, efficiency, size and dynamic response. With such emphasis on these performance requirements the need for using a multiphase synchronous boost converter arises. Selecting a number of phases can increase component counts and costs, which begs the question, how many phases are needed? What benefit do a given number of phases for a certain power requirement provide? This paper runs through a design example of a multiphase boost showing how interleaving affects cost, efficiency, size and performance.

The choice of using a non-isolated buck converter topology to reduce a distribution voltage to a lower one for point-of-load applications is an easy one. The buck is simple, has relatively few components and may be configured for a wide variety of applications. The choice of how to manage the control of the converter is not quite as straightforward a decision. This topic continues Topic 1: “Choosing the Right Fixed Frequency Buck Regulator Control Strategy” and shifts to the variable frequency realm with discussion of constant on-time control and its enhancements with various versions of the D-CAP architecture. The highlights and challenges for each technique are discussed and select design examples are presented.

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.

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