SHF: Small: Collaborative Research: Exploring Energy-Efficient GPGPUs Through Emerging Technology Integration

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Nowadays, graphics processing units (GPUs) have been widely adopted for general-purpose computing, and are known as GPGPUs. However, current and future GPGPUs confront power and energy as the dominant constraints. The number of transistors integrated on a single GPU chip continues to increase due to shrinking feature size and the demand for massively parallel computing cores to increase throughput. On the other hand, the continuous decrease of transistor supply voltage at each new technology node has largely stalled because of leakage constraints, leading to an ever-increasing power density. Therefore, future GPGPUs must become more inherently energy efficient to avoid hitting the power wall. To meet the increasing demands on performance and energy-efficiency, emerging technologies such as non-volatile memory, inter-bank tunneling field effect transistors (TFETs), silicon nanophotonics, and three-dimensional (3D) integration are being deployed in hardware design and promise realization of power efficiency at a scale never expected before. The investigators are exploring a synergetic program to holistically and hierarchically improve the GPGPU's energy efficiency through emerging technology integration. The project objectives include (1) non-volatile memory in the GPU computing cores and low-power mechanisms to substantially reduce leakage and dynamic power consumption; (2) a hybrid TFET-CMOS (complementary metal-oxide semiconductor) methodology to effectively address the energy challenge at both intra- and inter-core levels; (3) a novel 3D-stacked throughput architecture based on silicon-nanophotonics technology to improve memory access performance yet reduce power consumption; (4) integration of the key research innovations and cross-technology optimizations to fully explore the potential of GPGPU design enabled by these emerging technologies. The proposed research will facilitate GPGPUs staying on track with deep sub-micron scaling and meeting the increasing demand for high-performance computing, and will hence benefit numerous real-life applications. This project will also contribute to society through engaging high-school and undergraduate students from minority-serving institutions in research, attracting women and other under-represented groups into graduate education, expanding the computer engineering curriculum with GPGPU power modeling and optimization techniques, disseminating research infrastructure for education and training, and collaborating with the GPU R&D industry.