Dirk Groeneveld, Iz Beltagy, Evan Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, William Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah Smith, Hannaneh Hajishirzi. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, Chris Callison-Burch. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Motion estimation is the most time consuming stage of MPEG family encodings and it reportedly absorbs up to 90% of the total execution time of MPEG processing. Therefore, we propose a hardware/software co-design paradigm that uses a PIM module to efficiently execute motion estimation operations. We use a PIM module to reduce the memory access penalty caused by a large number of memory accesses. We segment the PIM module into small pieces so that each smaller PIM module can execute the operations in parallel fashion. However, in order to execute the operations in parallel, there are critical overheads that involve replicating a huge amount of data to many of these smaller PIM modules. Not only do these replications require a huge amount of additional memory accesses but also calculations when generating addresses. Therefore, we also present an efficient data distribution mechanism to effectively support parallel executions among these smaller PIM modules. With our paradigm, the host processor can be relieved from computationally-intensive and data-intensive workloads of motion estimation. We observed up to 2034/spl times/ improvement in reduction of the number of memory accesses and up to 439/spl times/ performance improvement for the execution of motion estimation operations when using our computing paradigm.

Procurement, the process of obtaining materials or services, is a critical process for any organization. While procuring a set of items from different suppliers who may sell only a subset (bundle) of a desired set of items, it will be required to select an optimal set of suppliers who can supply the desired set of items. This is the optimal vendor selection problem. Bundling in procurement has benefits such as demand aggregation, supplier aggregation, and lead-time reduction. The NP-hardness of the vendor selection problem motivates us to formulate a compatible linear programming problem by relaxing the integer constraints and imposing additional constraints. The newly formulated problem can be solved by a novel iterative algorithm proposed recently in the literature. In this paper, we show that the application of this iterative algorithm leads to an iterative procurement auction that improves the efficiency of the procurement process. By using reinforcement learning to orchestrate the iterations of the algorithm, we show impressive gains in computational efficiency of the algorithm.