COMBINATION OF MULTIPLE DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS FOR A TARGET HARDWARE

ACCELERATION OF NEURAL NETWORKS USING DEPTH - FIRST PROCESSING

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

The increased interest in Artificial Intelligence (AI) raised the need for highly optimized and sophisticated AI frameworks. Starting with the Lua-based Torch many frameworks have emerged over time, such as Theano, Caffe, Chainer, CNTK, MxNet, PyTorch, DL4J, or TensorFlow.

All of these provide a high level scripting API that allows users to easily design neural networks and run these on various kinds of hardware. What the user usually does not see is the high effort put into these frameworks to provide peak execution performance.

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SOL: Single middleware for optimized multi-architecture AI training and deployment

NEC User Group Meeting

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

High Performance Machine Learning (HPML)‘20

SOL4VE: Bringing Deep Neural Networks to the NEC SX-Aurora TSUBASA

NEC User Group Meeting

SOL4VE: Running Deep Neural Networks on the NEC SX-Aurora Tsubasa