RESUMO
In the field of edge computing, quantizing convolutional neural networks (CNNs) using extremely low bit widths can significantly alleviate the associated storage and computational burdens in embedded hardware, thereby improving computational efficiency. However, such quantization also presents a challenge related to substantial decreases in detection accuracy. This paper proposes an innovative method, called Adaptive Global Power-of-Two Ternary Quantization Based on Unfixed Boundary Thresholds (APTQ). APTQ achieves adaptive quantization by quantizing each filter into two binary subfilters represented as power-of-two values, thereby addressing the accuracy degradation caused by a lack of expression ability of low-bit-width weight values and the contradiction between fixed quantization boundaries and the uneven actual weight distribution. It effectively reduces the accuracy loss while at the same time presenting strong hardware-friendly characteristics because of the power-of-two quantization. This paper extends the APTQ algorithm to propose the APQ quantization algorithm, which can adapt to arbitrary quantization bit widths. Furthermore, this paper designs dedicated edge deployment convolutional computation modules for the obtained quantized models. Through quantization comparison experiments with multiple commonly used CNN models utilized on the CIFAR10, CIFAR100, and Mini-ImageNet data sets, it is verified that the APTQ and APQ algorithms possess better accuracy performance than most state-of-the-art quantization algorithms and can achieve results with very low accuracy loss in certain CNNs (e.g., the accuracy loss of the APTQ ternary ResNet-56 model on CIFAR10 is 0.13%). The dedicated convolutional computation modules enable the corresponding quantized models to occupy fewer on-chip hardware resources in edge chips, thereby effectively improving computational efficiency. This adaptive CNN quantization method, combined with the power-of-two quantization results, strikes a balance between the quantization accuracy performance and deployment efficiency in embedded hardware. As such, valuable insights for the industrial edge computing domain can be gained.
RESUMO
Atmospheric exposure is an important pathway of accumulation of lead (Pb) in Oryza sativa L. grains. In this study, source contributions of soil, early atmospheric exposure, and late atmospheric exposure, along with their bioaccumulation ratios were examined both in the pot and field experiments using stable Pb isotope fingerprinting technology combined with a three-compartment accumulation model. Furthermore, genotype differences in airborne Pb accumulation among four field-grown rice cultivars were investigated using the partial least squares path model (PLS-PM) linking rice Pb accumulation to agronomic traits. The findings revealed that during the late growth period, the air-foliar-grain transfer of Pb was crucial for rice Pb accumulation. Approximately 69-82% of the Pb found in polished rice was contributed by atmospheric source, with more than 80% accumulating during the late growth stage. The air accumulation ratios of rice grains were genotype-specific and estimated to be 0.364-1.062 m3/g during the late growth. Notably, grain size exhibited the highest standardized total effects on the airborne Pb concentrations in the polished rice, followed by leaf Pb and the upward translocation efficiency of Pb. The present study indicates that mitigating the health risks associated with Pb in rice can be achieved by controlling atmospheric Pb levels during the late growth stage and choosing Japonica inbred varieties characterized by large grain size.