Accelerated AI

FPGAs-as-a-Service Toolkit (FaaST)

Heterogeneous computing has the potential for significant gains over traditional computing models. This work represents the first open-source FPGAs-as-a-service toolkit.

GPU coprocessors as a service for deep learning inference in high energy physics

Graphics Processing Units can accelerate algorithms to resolve tremendously growing demands for computing in large scientific experiments. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics.

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model.