(* denotes equal contribution)
Ramyad Hadidi, Nima Shoghi, Bahar Asgari, Hyesoon Kim
Published in IEEE International Conference on Edge Computing and Communications, 2023
Presents a new context-aware approach for handling tasks in real-time on resource-constrained robots, achieving increased execution speed by integrating a dynamic time-sharing mechanism, event-driven scheduling, and lightweight virtualization.
Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Félix Therrien, Jehad Abed, Oleksandr Voznyy, Edward Sargent, Zachary Ulissi, C. Zitnick
Published in ACS Catalysis, 2023
Introduces the open source OC22 dataset, containing relaxations across various oxide materials, coverages, and adsorbates, to improve machine learning models for oxide electrocatalysts. The paper also establishes clear benchmarks for future efforts in this area, opens the data and models for community development, and introduces a public leaderboard.
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati, Nima Shoghi, Abhishek Das, Brandon Wood, C Zitnick, John Kitchin, Zachary Ulissi
Published in ACS Catalysis, 2022
Examines the challenges and limited generalizability of current machine-learning models for catalyst discovery and discusses the potential advancements brought about by large-scale catalyst data sets like OC20.
Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C Zitnick, Zachary Ulissi
Published in The Journal of Chemical Physics, 2022
Introduces TAAG, a novel attention-based transfer learning approach for Graph Neural Networks which significantly improves performance for out-of-domain datasets and speeds up model training, demonstrating the potential for generalizing important aspects across different atomic system domains.
Nima Shoghi*, Andrei Bersatti*, Moinuddin Qureshi, Hyesoon Kim
Published in IEEE Computer Architecture Letters, 2021
Introduces Smart Quantization (SmaQ), a quantization scheme that leverages the normal distribution properties of neural network data structures, leading to a memory usage reduction of up to 6.7x during training, with minimal impact on accuracy.
Ramyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette, Nima Shoghi, Hyesoon Kim
Published in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021
Examines the inherent design complexities in autonomous drones—especially the trade-offs between compute, energy, and electromechanical resources—and proposes a systematic exploration of the drone design space. The study emphasizes the benefits of optimizing the SLAM process on FPGA platforms and introduces a customizable, open-source drone.
Nima Shoghi, Ramyad Hadidi, Lee Jaewon, Jun Chen, Arthur Siqueria, Rahul Rajan, Shaan Dhawan, Pooya Shoghi, Hyesoon Kim
Published in arXiv preprint arXiv:2011.08936, 2020
Introduces a unique location-aware protocol for secure communication in Intelligent Transportation Systems, leveraging in situ visual localization (like QR codes) for efficient message verification. The new method is efficient, scalable, and infrastructure-independent, providing a more trustworthy and widely applicable solution than previous approaches.
Sam Jijina, Adriana Amyette, Nima Shoghi, Ramyad Hadidi, Hyesoon Kim
Published in 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2020
Conducts a detailed analysis of drone operation and efficiency by exploring hardware and software components, using ArduCopter as an example. Optimizing specific aspects of these components can significantly increase drone flight range.
Bahar Asgari, Ramyad Hadidi, Nima Shoghi, Hyesoon Kim
Published in 2020 57th ACM/IEEE Design Automation Conference (DAC), 2020
Introduces Pisces, a method that optimizes power consumption and latency for simultaneous localization and mapping (SLAM). Through using sparse data and reducing memory access, it results in a 2.5 times power reduction and 7.4 times faster execution than other contemporary methods.
Andrei Bersatti*, Nima Shoghi*, Hyesoon Kim
Published in The International Symposium on Memory Systems, 2020
Introduces NNW-BDI, a specialized memory compression scheme for neural network weights, successfully decreasing memory usage by up to 85% without diminishing inference accuracy.
Nima Shoghi, Ramyad Hadidi, Hyesoon Kim
Published in Student Research Competition at Embedded System Week (SRC ESWEEK), 2019
Examines the effectiveness of the ORBSLAM2 algorithm on the Raspberry Pi for real-time usage in embedded robots, identifying the Pi's slower performance, but proposing optimizations that nearly quintuple its speed with minimal precision loss, enabling real-time operation.