Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays

Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor.

3D MICROSENSOR IMAGING ARRAYS NETWORKS

2D microsensor arrays can permit spatial distribution measurements of the sensed parameter and enable high resolution sensing visualizations. Measuring constituents in a flowing media, such as air or liquid could benefit from such flow through or flow across imaging systems. These flow imagers can have applications in mobile robotics and non-visible imagery, and alternate mechanical systems of perception, process control and environmental observations. In order to create rigid-conformal, large area imaging systems we have in the past merged flexible PCB substrates with rigid constructions from 3D printing. This approach merges the 2D flexible electronics world of printed circuits with the 3D printed packaging world. Extending this 2D flow imaging concept into the third dimension permits 3D flow imaging networks, architectures and designs and can create a new class of sensing systems. Using 3D printing, 3D printed filaments, nets and microsensor cages, can be combined into integrated designs to generate distributed 3D imaging networks and camera systems for a variety of sensory applications.