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Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research 1 , chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area. To achieve this, we pose chip floorplanning as a reinforcement learning problem, and develop an edge-based graph convolutional neural network architecture capable of learning rich and transferable representations of the chip. As a result, our method utilizes past experience to become better and faster at solving new instances of the problem, allowing chip design to be performed by artificial agents with more experience than any human designer. Our method was used to design the next generation of Google’s artificial intelligence (AI) accelerators, and has the potential to save thousands of hours of human effort for each new generation. Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields. Machine learning tools are used to greatly accelerate chip layout design, by posing chip floorplanning as a reinforcement learning problem and using neural networks to generate high-performance chip layouts.

The process of managing data from measurements involves a set of methods and algorithms to explore and analyze them in order to determine specific structures that reproduce as accurately as possible the essence of the information resulting from them. This paper focuses on the statistical processing and interpretation of measurement data sets derived from measurements in web applications using three SAP UI technologies: Web Dynpro ABAP, Floorplan Manager, and CRM WebClient UI. Aspects related to determining the main statistical parameters, eliminating the values affected by gross errors, checking the randomness of the data, determining the distribution functions corresponding to each data sample, and checking the distributions based on concordance tests are considered.

Floorplans are commonly used to represent the layout of buildings. Research works toward computational techniques that facilitate the design process, such as automated analysis and optimization, often using simple floorplan representations that ignore the space’s semantics and do not consider usage-related analytics. We present a floorplan embedding technique that uses an attributed graph to model the floorplans’ geometric information, design semantics, and behavioral features as the node and edge attributes. A long short-term memory (LSTM) variational autoencoder (VAE) architecture is proposed and trained to embed attributed graphs as vectors in a continuous space. A user study is conducted to evaluate the coupling of similar floorplans retrieved from the embedding space for a given input (e.g., design layout). The qualitative, quantitative, and user study evaluations show that our embedding framework produces meaningful and accurate vector representations for floorplans. Besides, our proposed model is generative. We studied and showcased its effectiveness for generating new floorplans. We also release the dataset that we have constructed. We include the design semantic attributes and simulation-generated human behavioral features for each floorplan in the dataset for further study in the community.

In recent years, Visual Crowdsensing (VCS) that sensed through images and video, has become a predominant sensing paradigm of Mobile Crowdsensing (MCS), which is one of the current research hotspots. VCS requires people to capture the details of sensing objects in the real world in the form of pictures or video, which is widely used in various fields. However, there is no article summarizing the development and current situation of VCS. To this end, this paper summarized the latest applications of VCS, including floor plan generation, indoor scene reconstruction, outdoor scene reconstruction, event reconstruction, indoor localization, indoor navigation and disaster relief, and summarized some unique problems of VCS at present.

Seminal innovations in the mathematical field of applied optimization, such as a method known as simulated annealing4, have been motivated by the challenge of chip placement. Because macro blocks can be thousands or even millions of times larger than standard cells, placing cells and blocks simultaneously is extremely challenging. The authors report that, when their agent is pre-trained on a set of 10,000 chip floorplans, it is already quite successful when used in a 'one shot' mode on a new design: with no more than six extra hours of fine-tuning steps, the agent can produce floorplans that are superior to those developed by human experts for existing chips. [...]the agent's solutions are very different from those of trained human experts (Fig. 1). The trained agent's macro-block placements somehow evade such landmines in the design process, achieving superhuman outcomes for timing (ensuring that signals produced in the chip arrive at their destinations on time) and for the feasibility and efficiency with which wiring can be routed between components. [...]Mirhoseini and colleagues' use of simple metrics as proxies for key parameters of the chip design works surprisingly well - it will be interesting to understand why these proxies are so successful.

Humans spend over 90% of their time in buildings, which account for 40% of anthropogenic greenhouse gas emissions and are a leading driver of climate change. Incentivizing more sustainable construction, building codes are used to enforce indoor comfort standards and minimum energy efficiency requirements. However, they currently only reward measures such as equipment or envelope upgrades and disregard the actual spatial configuration and usage. Using a new hypergraph model that encodes building floorplan organization and facilitates automatic geometry creation, we demonstrate that space efficiency outperforms envelope upgrades in terms of operational carbon emissions in 72%, 61% and 33% of surveyed buildings in Zurich, New York, and Singapore. Using automatically generated floorplans in a case study in Zurich further increased access to daylight by up to 24%, revealing that auto-generated floorplans have the potential to improve the quality of residential spaces in terms of environmental performance and access to daylight. In this work, authors report a method to describe, evaluate and generate floor plans using hypergraphs. With it, it is shown how spatial efficiency has larger energy saving potential than traditional building upgrade measures and how autogenerated floor plans can increase comfort and building performance.

The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult to reconstruct a complete and accurate floorplan. It is well known that a suitable dataset is a key point to drive an effective algorithm, while existing datasets of floorplan reconstruction are synthetic and small. Without reliable accumulations of real datasets, the robustness of methods to real scene reconstruction is weakened. In this paper, we first annotate a large-scale realistic benchmark, which contains RGBD image sequences and 3D models of 80 indoor scenes with more than 10,000 square meters. We also introduce a framework for the floorplan reconstruction with mesh-based point cloud normalization. The loose-Manhattan constraint is performed in our optimization process, and the optimal floorplan is reconstructed via constraint integer programming. The experimental results on public and our own datasets demonstrate that the proposed method outperforms FloorNet and Floor-SP.

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design.