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Energy harvesting has been widely investigated as a promising method of providing power for ultra-low-power applications. Such energy sources include solar energy, radiofrequency (RF) radiation, piezoelectricity, thermal gradients, etc. However, the power supplied by these sources is highly unreliable and dependent upon ambient environment factors. Hence, it is necessary to develop specialized systems that are tolerant to this power variation, and also capable of making forward progress on the computation tasks. The simulation platform in this paper is calibrated using measured results from a fabricated nonvolatile processor and used to explore the design space for a nonvolatile processor with different architectures, different input power sources, and policies for maximizing forward progress.

Nonvolatile processors offer a number of desirable properties including instant on/off, zero standby power and resilience to power failures. This paper presents a fabricated nonvolatile processor based on ferroelectric flip-flops. These flipflops are used in a distributed fashion and are able to maintain system states without any power supply indefinitely. An efficient controller is employed to achieve parallel reads and writes to the flip-flops. A reconfigurable voltage detection system is designed for automatic system backup during power failures. Measurement results show that this nonvolatile processor can operate continuously even under power failures occurring at 20 KHz. It can backup system states within 7 μs and restore them within 3 μs. Such capabilities will provide a new level of support to chip-level fine-grained power management and energy harvesting applications.

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Energy harvesting is gaining more and more attentions due to its characteristics of ultra-long operation time without maintenance. However, frequent unpredictable power failures from energy harvesters bring performance and reliability challenges to traditional processors. Nonvolatile processors are promising to solve such a problem due to their advantage of zero leakage and efficient backup and restore operations. To optimize the nonvolatile processor design, this paper proposes new metrics of nonvolatile processors to consider energy harvesting factors for the first time. Furthermore, we explore the nonvolatile processor design from circuit to system level. A prototype of energy harvesting nonvolatile processor is set up and experimental results show that the proposed performance metric meets the measured results by less than 6.27% average errors. Finally, the energy consumption of nonvolatile processor is analyzed under different benchmarks.

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As the emerging trend of the graph-based deep learning, Graph Neural Networks (GNNs) recently attract a significant amount of research attention from various domains. However, existing GNN implementations fail to catch up with the evolving GNN architectures, the ever-increasing graph size, and node-embedding dimensionality, thus, suffering from an unsatisfied performance. To break this hurdle, we propose GN-NAdvisor, an efficient runtime system to systematically accelerate GNN applications on GPUs. First, GNNAdvisor spots the graph-structure information (e.g., graph community) as a new driving force to facilitate GNN acceleration. Besides, GNNAdvisor implements a novel yet highly-efficient groupbased workload management tailored for GNN computation to improve the thread-level performance on GPUs. GNNAdvisor further capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure. Moreover, GNNAdvisor incorporates a Modeling & Estimating strategy to offer sufficient flexibility for automatic performance tuning across various GNN architectures and input datasets. Extensive experiments show that GNNAdvisor provides average 3.02×, 4.36×, and 52.16× speedup over the state-of-the-art GNN execution frameworks, Deep Graph Library, NeuGraph, and GunRock, respectively.