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1. uBrain: a unary brain computer interface

   https://doi.org/10.1145/3470496.3527401  

   Wu, Di; Li, Jingjie; Pan, Zhewen; Kim, Younghyun; San Miguel, Joshua (June 2022, ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture)
2. uSystolic: Byte-Crawling Unary Systolic Array

   https://doi.org/10.1109/HPCA53966.2022.00010  

   Wu, Di; San Miguel, Joshua (April 2022, 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA))
3. uGEMM: Unary Computing Architecture for GEMM Applications

   https://doi.org/10.1109/ISCA45697.2020.00040  

   Wu, Di; Li, Jingjie; Yin, Ruokai; Hsiao, Hsuan; Kim, Younghyun; San Miguel, Joshua (May 2020, 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA))

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4. Clique Is Hard on Average for Unary Sherali-Adams

   https://doi.org/10.1109/FOCS57990.2023.00008  

   De Rezende, Susanna F.; Potechin, Aaron; Risse, Kilian (November 2023, 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS))

   We prove that unary Sherali-Adams requires proofs of size n^Ω(d) to rule out the existence o f an n^Θ(1)-clique in Erdős-Rényi random graphs whose maximum clique is of size d ≤ 2log n. This lower bound is tight up to the multiplicative constant in the exponent. We obtain this result by introducing a technique inspired by pseudo-calibration which may be of independent interest. The technique involves defining a measure on monomials that precisely captures the contribution of a monomial to a refutation . This measure intuitively captures progress and should have further applications in proof complexity. 

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5. uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional Computing

   https://doi.org/10.23919/DATE58400.2024.10546545  

   Aygun, Sercan; Moghadam, Mehran Shoushtari; Najafi, M Hassan (March 2024, IEEE)

   Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, the optimal design of hypervectors with high model accuracy requires powerful computing platforms for training. A more efficient approach is to generate hypervectors dynamically during the training phase. To this aim, this work uses low-discrepancy sequences to generate intensity hypervectors, while avoiding position hypervectors. Doing so eliminates the multiplication step in vector encoding, resulting in a power-efficient HDC system. For the first time in the literature, our proposed approach employs lightweight vector generators utilizing unary bit-streams for efficient encoding of data instead of using conventional comparator-based generators. 

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