CSR: Small: Collaborative Research:Heterogeneous Ultra Low Power Accelerator for Wearable Biomedical Computing

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With the rapid advances in small, low-cost wearable computing technologies, there is a tremendous opportunity to develop personal health monitoring devices capable of continuous vigilant monitoring of physiological signals. Wearable biomedical devices have the potential to reduce the morbidity, mortality, and economic cost associated with many chronic diseases by enabling early intervention and preventing costly hospitalizations. These low power systems require to have the capacity to provide fast and accurate processing and interpretation of vast amounts of data and generate smart alarms only when warranted. The objective of this project is to build the foundation of the next generation of heterogeneous biomedical signal processing platforms that can address the current and future generation energy-efficiency requirements and computational demands. The PIs start with understanding the specific characteristics of emerging biomedical signal and imaging applications on off-the-shelf embedded low power multicore CPU, GPU and FPGA platforms to accurately understand the trade-offs they offer and the bottlenecks they have. Based on these results, the PIs will design and architect a domain-specific manycore accelerator in hardware and integrate it with an off-the-shelf embedded processor that together combine performance, scalability, programmability, and power efficiency requirements for these applications. The PIs will implement the proposed heterogeneous architecture in hardware and will evaluate its performance and power efficiency with a number of real-life biomedical workloads including seizure detection, handheld ultrasound spectral Doppler and imaging, tongue drive assistive device and prosthetic hand control interface.The proposed interdisciplinary research effort could inspire and enable new approaches to healthcare monitoring, and can significantly impact several fields including human-centered cyber-physical systems, cyber-security, mobile communications, bioinformatics and applications that require high performance and energy efficient embedded computing from different sensors. The proposed benchmark, characterization, and software-hardware computing framework will be freely shared and broadly disseminated among colleagues in related disciplines. Research results will be integrated in graduate and undergraduate courses offered by the investigators in both campuses. The PIs are active in several campus-wide and national organizations that work to attract and retain members of under-represented groups to engage in research and complete graduate degrees in science and engineering.