Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results formanymachine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embeddedsystems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called “scatter-and-gather” to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {−1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.
Cooking has been proven to be a significant source of primary organic aerosol, especially in megacities. However, the formation of secondary organic aerosol (SOA) derived from cooking emissions is still poorly understood. In this work, four prevalent Chinese domestic cooking types involving complicated cuisines and various cooking methods were chosen to conduct a lab simulation for SOA formation using a Gothenburg potential aerosol mass reactor (Go: PAM). After samples had been aged under OH exposures of 4.3–27.1 × 1010 molecules cm–3 s, the domestic cooking SOA was characterized by mass growth potentialities (1.81–3.16), elemental ratios (O/C = 0.29–0.41), and mass spectra. Compared with other organic aerosol (OA), domestic cooking SOA is a kind of less oxidized oxygenated OA (LO-OOA) with a unique oxidation pathway (alcohol/peroxide pathway) and mass spectra (characteristic peaks at m/z 28, 29, 41, 43, 44, 55, and 57). This study is expected to identify the cooking SOA under actual cooking conditions, which could contribute to the formulation of pollution source control as well as the health risk assessment of exposure to cooking fumes.
Membrane separation has enjoyed tremendous advances in relevant material and engineering sciences, making it the fastest growing technology in water treatment. Although membranes as a broad-spectrum physical barrier have great advantages over conventional treatment processes in a myriad of applications, the need for higher selectivity and specificity in membrane separation is rising as we move to target contaminants at trace concentrations and to recover valuable chemicals from wastewater with low energy consumption. In this review, we discuss the drivers, fundamental science, and potential enabling materials for high selectivity membranes, as well as their applications in different water treatment processes. Membrane materials and processes that show promise to achieve high selectivity for water, ions, and small molecules—as well as the mechanisms involved—are highlighted. We further identify practical needs, knowledge gaps, and technological barriers in both material development and process design for high selectivity membrane processes. Finally, we discuss research priorities in the context of existing and future water supply paradigms.