nGraph: Unlocking Next-generation Performance with Deep Learning Compilers

13 Sep, 2019

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nGraph: Unlocking Next-generation Performance with Deep Learning Compilers

The rapid growth of deep learning in large-scale real-world applications has led to a rapid increase in demand for high-performance training and inference solutions. This demand is reflected in increased investment in deep learning performance by hardware manufacturers, and includes a proliferation of new application-specific accelerators.

But performance isn’t driven by hardware alone. In the software realm, a new class of deep learning compilers has emerged, which brings to bear both classic and novel compiler techniques in order to maximize the performance of deep learning systems. Recently developed deep learning compilers include NNVM/TVM from the University of Washington, Glow from Facebook, XLA from Google, and nGraph from Intel. These deep learning compilers unlock a wealth of optimizations that encompass the whole data-flow graph. This approach achieves substantial speedups over the approach favored by existing frameworks, in which an interpreter orchestrates the invocation of per-op compute kernels that must be optimized specifically for the framework and hardware target. This webinar will offer a comprehensive overview of Intel’s nGraph deep learning compiler.

Adam Procter

Adam Procter is a deep learning software engineer in the Artificial Intelligence Products Group at Intel, where he works on the core design of the Intel nGraph deep learning compiler. He holds a PhD in computer science from the University of Missouri, where his research focused on programming language semantics, high-assurance computing, and techniques for compiling functional programming languages to reconfigurable hardware.