An Optimized Recurrent Unit for UltraLowPower Keyword Spotting. There is growing interest in being able to run neural networks on sensors, wearables and internetofthings (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resourceconstrained edge devices.  :34,meet this need, our work introduces a new recurrent unit architecture that is specifically adapted for ondevice low power acoustic event detection (AED). The proposed architecture is based on the gated recurrent unit (GRUu0027) but features optimizations that make it implementable on ultralow power microcontrollers such as the Arm Cortex M0.  new :86,architecture, the Embedded Gated Recurrent Unit (eGRU) is demonstrated to be highly efficient and suitable for shortduration AED and keyword spotting tasks. A single eGRU cell is 60x faster and 10x smaller than a GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks of varying complexities.  :137,practicality of eGRU is investigated in a wearable acoustic event detection application. An eGRU model is implemented and tested on the Arm Cortex M0based Atmel ATSAMD21E18 processor. The Arm M0 implementation of the eGRU model compares favorably with a full precision GRU that is running on a workstation. The embedded eGRU model achieves a classification accuracy 95.3%, which is only 2% less than the full precision GRU.

EnergyQuality Scalable AnalogtoDigital Conversion and Machine Learning Engine in a 51.9 nJframe Voice Activity Detector In this work, voice activity detection (VAD) system with dynamic energyquality (EQ) scalability is presented. EQ scalability is enabled through the insertion of multiple knobs at different levels of the signal chain, starting from analogdigital conversion and ending at the classification stage. Such knobs are cooptimized at runtime to achieve a given quality target with minimal energy. Such cooptimization is also shown to improve the fit of the machine learning algorithm, allowing for more graceful quality degradation. The proposed system, fabricated on a 28nm test chip, classifies at 81.2% accuracy while consuming 51.9 nJframe in a 10dB noise context. Scaling up energy consumption by 3.5x improves accuracy by 5.2%.