Paper discusses the challenges and opportunities of machine learning hardware.
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data 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. This paper discusses how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies.