Neural Networks are foundational AI constructs for recognizing relationships in data requiring processing massive datasets in the form of tensors. Tensor processing is central to AI and machine learning applications. Algorithms such as convolutions and pooling involve running operations on matrix and vector data structures. It is these operations that can be executed efficiently on processors that are designed to allow many common operations to run in parallel across many cores, this has become a common use case for GPUs in recent years. But companies are also looking at ways to develop more specialised processors to tackle the performance challenges of modern AI applications. This presentation will explain how our team accelerated the execution of a tensor-based neural network on the RISC-V Spike simulator using open source and open standard software. We will explore how our demo application was built using the open source Eigen, SYCL-BLAS and SYCL-DNN libraries with the ResNet50 neural network commonly used for processor benchmarks.
There will be an emphasis on the open source software we use and develop to enable our software stack.
Charles is Chief Business Officer and has been with Codeplay since 2014. Charles graduated from Glasgow University with an honours degree in Electronic Systems and Microprocessor Engineering. Charles then followed a career doing ASIC chip design in GEC Plessey Semiconductors and Pioneer, applications engineering and marketing with VLSI/Philips/NXP in South France, and product marketing director with Broadcom® in Cambridge for mobile multimedia solutions used by Nokia®, Samsung® and Raspberry Pi®.