ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement
This work addresses the need for energy-efficient and real-time SAM on mobile devices, which is crucial for mental health tracking and IoT applications, though it is incremental as it builds on existing neural architecture search methods.
The paper tackles the problem of on-device social ambiance measurement (SAM) for privacy-sensitive applications by proposing ERSAM, a neural architecture search framework that achieves an error rate of 14.3% while consuming only 40 mW x 12 h energy and 0.05 seconds latency on a Pixel 3 phone.
Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facilitate wide adoption of the aforementioned applications, the required computational complexity of state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at odds with the often constrained resources on mobile devices. Furthermore, only limited labeled data is available or practical when it comes to SAM under clinical settings due to various privacy constraints and the required human effort, further challenging the achievable accuracy of on-device SAM solutions. To this end, we propose a dedicated neural architecture search framework for Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework can automatically search for DNNs that push forward the achievable accuracy vs. hardware efficiency frontier of mobile SAM solutions. For example, ERSAM-delivered DNNs only consume 40 mW x 12 h energy and 0.05 seconds processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only achieving an error rate of 14.3% on a social ambiance dataset generated by LibriSpeech. We can expect that our ERSAM framework can pave the way for ubiquitous on-device SAM solutions which are in growing demand.