Power-Efficient Long Short-term Memory

If artificial intelligence could be embedded into small, self-powered sensors, it would be useful in the following scenarios. In precision agriculture, such sensors could forecast frost to avoid crop damage; monitor the local bird population for harmful versus beneficial species; and assess soil conditions for efficient irrigation. In smart factories, such sensors could be used for predictive maintenance, thus minimizing the costly downtime that is caused both by undetected equipment failure and by overly-frequent, scheduled servicing.

In consumer healthcare, such sensors could be worn on the body to detect sleep apnea, track mental health state or measure blood pressure, all without bulky, obtrusive batteries or the disruption of frequent recharging. Unfortunately, even in moderately complex applications, artificial intelligence requires more power than a self-powered sensor can provide.

To address this problem, we propose to develop a new type of artificial intelligence that requires no more power to run than is available in a self-powered sensor.

The goal of the research is to design, implement and evaluate an analog long short-term memory (LSTM) that is 16 times more power-efficient than the state-of-the-art. We will achieve this power efficiency with the following methods: we will use (1) fewer inputs and (2) fewer operations than the state-of-the-art. Our approach is compatible with conventional power reduction strategies like compute-in-memory, weight quantization, knowledge distillation or network pruning.

Further, our approach is robust to analog mismatch. We will demonstrate our approach in a keyword spotting task, although the same principles can be extended to other applications. The intellectual significance of the proposed activity is that it will advance the field of ubiquitous sensors by developing a new LSTM paradigm that is 16 times more power efficient than the state-of-the-art.

The resulting energy-efficient ubiquitous sensors will increase capabilities across different fields, from industrial to agricultural and consumer healthcare applications.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.