AWS Partner Network (APN) Blog
Author: Yoshitaka Haribara
Yoshitaka Haribara is a Sr. Startup Machine Learning and Quantum Solutions Architect at AWS, working with startup customers to leverage AWS cloud including quantum technologies such as Amazon Braket. He holds a PhD in Mathematical Informatics from the University of Tokyo where he conducted research in the field of combinatorial optimization using quantum optics.
Using Fewer Resources to Run Deep Learning Inference on Intel FPGA Edge Devices
Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.