This AI Paper from Intel Presents a SYCL Implementation of Fully Fused Multi-Layer Perceptrons (MLPs) on Intel Data Center GPU Max

 This AI Paper from Intel Presents a SYCL Implementation of Fully Fused Multi-Layer Perceptrons (MLPs) on Intel Data Center GPU Max

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Artificial Intelligence Solutions for Businesses

Introducing Fully Fused Multi-Layer Perceptrons (MLPs) on Intel Data Center GPU Max

In the realm of Artificial Intelligence (AI), Multi-Layer Perceptrons (MLPs) serve as the cornerstone for various Machine Learning (ML) tasks. They are instrumental in solving partial differential equations, representing density functions in Neural Radiance Fields (NeRFs), and simulating ray tracing using Neural Ray Tracing.

One key feature of MLPs is the fully connected layers, allowing every neuron in a layer to be connected to every other neuron in the layer above and below. This property enables MLPs to be used for fully fusing processes, essential for certain computational workloads.

A team of researchers from Intel Corporation and Ecole Polytechnique has focused on effectively building narrow MLPs on Intel GPUs. Narrow MLPs are universal approximators and, despite their limitations, are significant in various applications. The team has introduced fully-fused MLPs with a fixed layer width of 2^i neurons and arbitrary depth using SYCL for Intel GPUs. These MLPs have shown improved performance, particularly for 64-width MLPs, and have been applied to various benchmarks and applications to demonstrate their effectiveness.

The team’s primary contributions include the first SYCL implementation for fully-fused MLPs for Intel GPUs, validated performance improvements, and demonstration of effectiveness across a variety of activities and datasets.

Practical AI Solutions for Businesses

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