In-sensor information processing for resource-limited platforms on flexible epidermal substrates

Moving towards the age of big data, the demand for embedded processing has been drastically increasing to make inference and intelligent decisions at lower architectural layers. The myriad of health conditions that can be treated and analyzed via low-energy embedded information processing kernels drives the demand for biomedical circuits, with optimized performance and cost. A large class of these healthcare applications require digital signal processing algorithms to be implemented with strict resources, such as energy and silicon area. Shrinking technology nodes produce both higher computing performance and energy efficiency. However, energy delivery and communication circuitry have not benefited significantly from technology scaling due to different sets of figures of merit. In-sensor information processing can be utilized to lower the energy consumption of such systems by eliminating the redundant volume of data traffic between the sensors and the central processing station. This work focuses on embedding intelligence on the epidermal flexible substrates to extract and analyze critical biomedical information for in-situ diagnosis. The primary objective of this work is illustrating the advantages of epidermal electronics combined with robust information processing systems, at system and application level. The major challenge is the design of robust and efficient algorithms for reliable operation on resource limited hardware platforms and flexible substrate non-idealities. To do so, we developed the first in-sensor ECG and PPG processors on flexible epidermal substrates. The systems are first prototyped using discrete components, followed by an ASIC implementation. Measurement results show that the in-sensor information processing has reduced the transmitted data traffic by 150X, and the system energy consumption by 3.56X