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Page, Adam, Attaran, Nasrin, Shea, Colin, Homayoun, Houman, Mohsenin, Tinoosh.  2016.  Low-Power Manycore Accelerator for Personalized Biomedical Applications. Proceedings of the 26th Edition on Great Lakes Symposium on VLSI. :63–68.

Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.

Sasan, Avesta, Zu, Qi, Wamg, Yanzhi, Seo, Jae-sun, Mohsenin, Tinoosh.  2018.  Low Power and Trusted Machine Learning. Proceedings of the 2018 on Great Lakes Symposium on VLSI. :515–515.

In this special discussion session on machine learning, the panel members discuss various issues related to building secure and low power neuromorphic systems. The security of neuromorphic systems may be discussed in term of the reliability of the model, trust in the model, and security of the underlying hardware. The low power aspect of neuromorphic computing systems may be discussed in terms of adaptation of new devices and technologies, the adaptation of new computational models, development of heterogeneous computing frameworks, or dedicated engines for processing neuromorphic models. This session may include discussion on the design space of such supporting hardware, exploring tradeoffs between power/energy, security, scalability, hardware area, performance, and accuracy.