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Simplified Solutions: Easy-to-Use AI for Embedded Applications

Agenda

Optimizing AI for Embedded Systems and IoT Applications

AI experts often overlook the constraints of embedded systems, making it challenging to fit a conventional model into a tiny device. Typical embedded constraints include limited computational resources and power.

To address these, we reduce the complexity of the deep net inference engine by minimizing intra-network connectivity, eliminating floating-point data, and using only accumulation operations.

These small-footprint, low-latency deep nets are suitable for applications in IoT smart sensors measuring inertial, vibration, temperature, flow, electrical, and biochemical data in battery-powered endpoints. Applications include healthcare and industrial wearables, robots, and automotive systems.

Easy-to-use-AI-Altaf-Khan
Altaf Khan

CEO at Infxl

Altaf Khan is the CEO of Infxl LLC, Colleyville, TX. He started his career as an accelerometer system engineer in Silicon Valley, but simplifying neural nets has been his passion over the last three decades. He has developed fast deep nets for real-time applications, low-cost deep nets for battery-operated IoT endpoints, and small-footprint deep nets for FPGA, DSP, and MCU. He has developed intelligent solutions for a major US airline and a well-known auto parts supplier. He has been an academic, the CTO of a brokerage firm, CEO of two startups, a startup advisor, a consultant for software process improvement, and an industrial controls engineer. Altaf received his BSEE from Wilkes College, MSEE for the University of Pennsylvania, and PhD from the University of Warwick.

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