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1. Interactive Architectural Design with Diverse Solution Exploration

   https://doi.org/10.1109/TVCG.2019.2938961  

   Berseth, Glen ; Haworth, Brandon ; Usman, Muhammad ; Schaumann, Davide ; Khayatkhoei, Mahyar ; Kapadia, Mubbasir Turab ; Faloutsos, Petros ( September 2019 , IEEE Transactions on Visualization and Computer Graphics)

   In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training. 

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2. A 283 pJ/b 240 Mb/s Floating-Point Baseband Accelerator for Massive MU-MIMO in 22FDX

   https://doi.org/10.1109/ESSCIRC55480.2022.9911311  

   Castaieda, Oscar ; Benini, Luca ; Studer, Christoph ( September 2022 , IEEE 48th European Solid State Circuits Conference (ESSCIRC))

   We present PULPO, a floating-point baseband-processing accelerator for massive multi-user multiple-input multiple-output (MU-MIMO) basestations (BSs). PULPO accelerates matrix-vector products, not only with a matrix but also with its Hermitian, as well as affine transforms and nonlinear projections used in iterative algorithms that outclass traditional linear methods in various applications. PULPO is integrated in a system-on-chip (SoC) with a tight integration to the system's data memory, facilitating data exchange and co-operation with 8 RISC-V cores. The fabricated accelerator achieves comparable efficiency as recently-proposed fixed-point baseband processors, while eliminating the burdens associated with fixed-point design, thus simplifying massive MU-MIMO BS development. 

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3. Real-Time Multi-Modal Human–Robot Collaboration Using Gestures and Speech

   https://doi.org/10.1115/1.4054297  

   Chen, Haodong ; Leu, Ming C. ; Yin, Zhaozheng ( October 2022 , Journal of Manufacturing Science and Engineering)

   Abstract As artificial intelligence and industrial automation are developing, human–robot collaboration (HRC) with advanced interaction capabilities has become an increasingly significant area of research. In this paper, we design and develop a real-time, multi-model HRC system using speech and gestures. A set of 16 dynamic gestures is designed for communication from a human to an industrial robot. A data set of dynamic gestures is designed and constructed, and it will be shared with the community. A convolutional neural network is developed to recognize the dynamic gestures in real time using the motion history image and deep learning methods. An improved open-source speech recognizer is used for real-time speech recognition of the human worker. An integration strategy is proposed to integrate the gesture and speech recognition results, and a software interface is designed for system visualization. A multi-threading architecture is constructed for simultaneously operating multiple tasks, including gesture and speech data collection and recognition, data integration, robot control, and software interface operation. The various methods and algorithms are integrated to develop the HRC system, with a platform constructed to demonstrate the system performance. The experimental results validate the feasibility and effectiveness of the proposed algorithms and the HRC system. 

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4. Toward Fully Automated Machine Learning for Routability Estimator Development

   https://doi.org/10.1109/TCAD.2023.3330818  

   Chang, Chen-Chia ; Pan, Jingyu ; Xie, Zhiyao ; Zhang, Tunhou ; Hu, Jiang ; Chen, Yiran ( March 2024 , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   The rise of machine learning (ML) technology inspires a boom in its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs. However, manually crafting ML models remains a complex and time-consuming process because it requires extensive human expertise and tremendous engineering efforts to carefully extract features and design model architectures. In this work, we leverage automated ML techniques to automate the ML model development for routability prediction, a well-established technique that can help to guide cell placement toward routable solutions. We present an automated feature selection method to identify suitable features for model inputs. We develop a neural architecture search method to search for high-quality neural architectures without human interference. Our search method supports various operations and highly flexible connections, leading to architectures significantly different from all previous human-crafted models. Our experimental results demonstrate that our automatically generated models clearly outperform multiple representative manually crafted solutions with a superior 9.9% improvement. Moreover, compared with human-crafted models, which easily take weeks or months to develop, our efficient automated machine-learning framework completes the whole model development process in only 1 day. 

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5. On the Complexity and Approximability of Optimal Sensor Selection for Kalman Filtering

   https://doi.org/10.23919/ACC.2018.8431016  

   Ye, Lintao ; Roy, Sandip ; Sundaram, Shreyas ( June 2018 , 2018 Annual American Control Conference (ACC))

   Given a linear dynamical system, we consider the problem of selecting (at design-time) an optimal set of sensors (subject to certain budget constraints) to minimize the trace of the steady state error covariance matrix of the Kalman filter. Previous work has shown that this problem is NP-hard for certain classes of systems and sensor costs; in this paper, we show that the problem remains NP-hard even for the special case where the system is stable and all sensor costs are identical. Furthermore, we show the stronger result that there is no constant-factor (polynomial-time) approximation algorithm for this problem. This contrasts with other classes of sensor selection problems studied in the literature, which typically pursue constant-factor approximations by leveraging greedy algorithms and submodularity of the cost function. Here, we provide a specific example showing that greedy algorithms can perform arbitrarily poorly for the problem of design-time sensor selection for Kalman filtering. 

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