Wearable devices are used to monitor numerous health parameters, however, the amount of data generated by wearables was 1500 petabytes (one petabyte = one million gigabytes) in 2018 and is expected to grow at a massive CAGR of 168% to reach 4020 petabytes in 2020 . Normally, this large amount of data needs to be processed externally using high-power cloud computation systems, implying high power consumption for both data transmission and processing, as well as large storage requirements. Commercially available health monitoring devices are inept at processing this data locally and are based on microprocessors commonly found in computers as central processing units (CPUs) or graphic processing units (GPUs), or on specialized microprocessors such as digital signal processors (DSPs). Transferring, storage and processing of this data is expensive, especially in continuous monitoring applications. Thus, a large chunk of the data is not processed and identification of pathological events is not achieved. Additionally, traditional processors imply high power consumption for data analysis and processing, hence it is not feasible to integrate them in wearables due to their lower battery capacities. DSPs are currently used to meet intensive on-board processing needs demanded by medical applications. However, DSPs imply a power consumption between 2 and 3.5W running at 1 GHz , limiting their use to devices with large batteries, limiting usable device lifetime between recharges, and making continuous processing highly energy expensive. During the phase 2 project, aiCTX will look to upgrade the input and output modules and configuration modules, as well as the software so different configurations can eventually be loaded on the DynapIP chip for real medical device use cases. A production line will be set up at CMO Global Foundries to produce 100 units for demonstration and validation with Biovotion a medical device company.
