Location Analysis of Chain Supermarket Distribution Center Based on CFLP

The number of stores operated by chain supermarkets is large, and the distribution of goods has the characteristics of low delivery volume and multiple delivery times. Logistics transportation costs account for a large proportion of operating costs. Establishing distribution centers is an effective measure to reduce operating costs. Through large-scale distribution, the chain supermarket distribution center can centrally manage the inventory of goods and distribute them to each store, thereby improving the distribution efficiency. This paper analyzes the influencing factors of the location of chain convenience supermarket distribution center, and then qualitatively analyzes the actual situation of the location object, determines the initial location plan, and establishes the CFLP model to solve the problem of the location selection of the chain supermarket distribution center

DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks

With the growing demand for vision applications and deployment across edge devices, the development of hardware-friendly architectures that maintain performance during device deployment becomes crucial. Neural architecture search (NAS) techniques explore various approaches to discover efficient architectures for diverse learning tasks in a computationally efficient manner. In this paper, we present the next-generation neural architecture design for computationally efficient neural architecture distillation - DONNAv2 . Conventional NAS algorithms rely on a computationally extensive stage where an accuracy predictor is learned to estimate model performance within search space. This building of accuracy predictors helps them predict the performance of models that are not being finetuned. Here, we have developed an elegant approach to eliminate building the accuracy predictor and extend DONNA to a computationally efficient setting. The loss metric of individual blocks forming the network serves as the surrogate performance measure for the sampled models in the NAS search stage. To validate the performance of DONNAv2 we have performed extensive experiments involving a range of diverse vision tasks including classification, object detection, image denoising, super-resolution, and panoptic perception network (YOLOP). The hardware-in-the-loop experiments were carried out using the Samsung Galaxy S10 mobile platform. Notably, DONNAv2 reduces the computational cost of DONNA by 10x for the larger datasets. Furthermore, to improve the quality of NAS search space, DONNAv2 leverages a block knowledge distillation filter to remove blocks with high inference costs.Comment: Accepted at ICCV-Workshop on Resource-Efficient Deep Learning, 202