Architecture for Machine Learning Applications at the Edge

By SemiWiki

November 5, 2018

Machine learning applications in data centers (or “the cloud”) have pervasively changed our environment. Advances in speech recognition and natural language understanding have enabled personal assistants to augment our daily lifestyle. Image classification and object recognition techniques enrich our social media experience, and offer significant enhancements in medical diagnosis and treatment. These applications are typically based upon a deep neural network (DNN) architecture. DNN technology has been evolving since the origins of artificial intelligence as a field of computer science research, but has only taken off recently due to the improved computational throughput, optimized silicon hardware, and available software development kits (and significant financial investment, as well).

Although datacenter-based ML applications will no doubt continue to grow, an increasing focus is being applied to ML architectures optimized for “edge” devices. There are stringent requirements for ML at the edge – e.g., real-time throughput, power efficiency, and cost are critical constraints.

I recently spoke with Geoff Tate, CEO at Flex Logix Technologies, for his insights on ML opportunities at the edge, and specifically, a new product emphasis that FlexLogix is undertaking. First, a quick background on DNN’s.

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