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


AI experts often do not appreciate the constraints of embedded systems, and that the process of shoe-horning a conventional AI model into a tiny device can be overly complex and frustrating. We describe constraints that are typical to embedded implementations. We address these constraints by reducing the complexity of the deep net inference engine. We achieve that by minimizing the intra-network connectivity, eliminating the need for floating-point data, and replacing the multiply-accumulate operation with just accumulation. The resultant small-footprint, low-latency deep nets are suitable for embedded applications that employ any 8/16/32/64-bit MCU, DSP, and FPGA. This solution is targeted at IoT smart sensors for inertial, vibration, temperature, flow, electrical, and biochemical measurements in battery-powered endpoints. Applications include healthcare/industrial wearables, robots, and automotive.

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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