| Summary: | Machine learning is becoming increasingly important in this era of big data. It enables us to extract meaningful information from the overwhelming amount of data being generated and collected every day. This information can be used to analyze and understand the data to identify trends (e.g., surveillance and portable/wearable electronics) or to take immediate action (e.g., robotics/drones, self-driving cars, and smart Internet of Things). In many applications, embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns or limitations in the communication bandwidth. However, sensor devices often have stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Circuit designers can play an important role in addressing these challenges by developing energy-efficient platforms to perform the necessary processing for machine learning. In this article, we will give a short overview of the key concepts in machine learning, discuss its challenges particularly in the embedded space, and highlight various opportunities where circuit designers can help to address these challenges.
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