There are several mobile and server AI benchmarks in use today and some new ones on the horizon. Which of these or others are applicable to IoT use cases? How do you meaningfully compare AI performance across the wide range of IoT HW with widely varying cost, memory, power and thermal constraints, and accuracy tradeoffs for quantized models vs non-quantized models? This talk will discuss these topics and some of the possible ways to address the issues.
Director Engineering (Qualcomm Technologies Inc)
Presently in QCT for Qualcomm Technologies Inc (QTI), working on a Deep Learning framework for Qualcomm SoCs and as an open source software strategist. Mark has represented QTI on the Linux Foundation board, and served on the Dronecode board, and Core Infrastructure Initiative steering committee. He also contributed code to the PX4 Open Source Flight Stack (http://github.com/PX4/Firmware), the FIRST Robotics platform, to the LLVMLinux project, and patches to the Linux Kernel.