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1. Amortizing Embodied Carbon Across Generations

   Ji, Shixin; Zhuang, Jinming; Yang, Zhuoping; Jones, Alex; Zhou, Peipei (November 2024, IEEE)

   Data centers have been relying on renewable energy integration coupled with energy efficient specialized processing units and accelerators to increase sustainability. Unfortunately, the carbon generated from manufacturing these systems is be- coming increasingly relevant due to these energy decarbonization and efficiency improvements. Furthermore, it is less clear how to mitigate this aspect of embodied carbon. As workloads continue to evolve over each hardware generation we explore the tradeoffs of fabricating new application-tuned hardware compared with more general solutions such as Field Programmable Gate Arrays (FPGAs). We also explore how REFRESH FPGAs can amortize embodied carbon investments from previous generations to meet the requirements of future generations workloads. 

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2. Embodied Action Scaffolds Dialogic Reading

   https://doi.org/https://doi.org/10.1007/s10648-021-09617-6  

   Wall, Danielle; Foltz, Sarah; Kupfer, Anne (May 2021, Educational psychology review)

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3. HEAT - Human Embodied Autonomous Thermostat

   https://doi.org/10.1016/j.buildenv.2020.106879  

   Li, Da; Menassa, Carol C.; Kamat, Vineet R.; Byon, Eunshin (July 2020, Building and Environment)

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4. Embodied Self-Aware Computing Systems

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   Hoffmann, Henry; Jantsch, Axel; Dutt, Nikil D. (July 2020, Proceedings of the IEEE)
5. Multi-Target Embodied Question Answering

   Yu, Licheng; Chen, Xinlei; Gkioxari, Georgia; Bansal, Mohit; Berg, Tamara L; Batra, Dhruv (January 2019, IEEE Conference on Computer Vision and Pattern Recognition)

   Embodied Question Answering (EQA) is a relatively new task where an agent is asked to answer questions about its environment from egocentric perception. EQA as introduced in [8] makes the fundamental assumption that every question, e.g. “what color is the car?”, has exactly one target (“car”) being inquired about. This assumption puts a direct limitation on the abilities of the agent. We present a generalization of EQA – Multi-Target EQA (MT-EQA). Specifically, we study questions that have multiple targets in them, such as “Is the dresser in the bedroom bigger than the oven in the kitchen?”, where the agent has to navigate to multiple locations (“dresser in bedroom”, “oven in kitchen”) and perform comparative reasoning (“dresser” bigger than “oven”) before it can answer a question. Such questions require the development of entirely new modules or components in the agent. To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module. The program generator converts the given question into sequential executable sub-programs; the navigator guides the agent to multiple locations pertinent to the navigation-related sub-programs; and the controller learns to select relevant observations along its path. These observations are then fed to the VQA module to predict the answer. We perform detailed analysis for each of the model components and show that our joint model can outperform previous methods and strong baselines by a significant margin. Project page: https://embodiedqa.org. 

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