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   Park, Soohyung ; Glover, Kerney J. ; Im, Wonpil ( June 2019 , Journal of Computational Chemistry)
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   https://doi.org/10.1021/jacs.3c10042  

   Yao, Yang-Rong ; Zhao, Jing ; Meng, Qingyu ; Hu, Han-Shi ; Guo, Min ; Yan, Yingjing ; Zhuang, Jiaxin ; Yang, Shangfeng ; Fortier, Skye ; Echegoyen, Luis ; et al ( November 2023 , Journal of the American Chemical Society)
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   Moazzemi, Kasra ; Maity, Biswadip ; Yi, Saehanseul ; Rahmani, Amir M. ; Dutt, Nikil ( October 2019 , ACM Transactions on Embedded Computing Systems)
4. CANNON: C ommunication- A ware Sparse N eural N etwork O ptimizatio n

   https://doi.org/10.1109/TETC.2023.3289778  

   Goksoy, A. Alper ; Li, Guihong ; Mandal, Sumit K. ; Ogras, Umit Y. ; Marculescu, Radu ( January 2023 , IEEE Transactions on Emerging Topics in Computing)

   Sparse deep neural networks (DNNs) have the potential to deliver compelling performance and energy efficiency without significant accuracy loss. However, their benefits can quickly diminish if their training is oblivious to the target hardware. For example, fewer critical connections can have a significant overhead if they translate into long-distance communication on the target hardware. Therefore, hardware-aware sparse training is needed to leverage the full potential of sparse DNNs. To this end, we propose a novel and comprehensive communication-aware sparse DNN optimization framework for tile-based in-memory computing (IMC) architectures. The proposed technique, CANNON first maps the DNN layers onto the tiles of the target architecture. Then, it replaces the fully connected and convolutional layers with communication-aware sparse connections. After that, CANNON optimizes the communication cost with minimal impact on the DNN accuracy. Extensive experimental evaluations with a wide range of DNNs and datasets show up to 3.0× lower communication energy, 3.1× lower communication latency, and 6.8× lower energy-delay product compared to state-of-the-art pruning approaches with a negligible impact on the classification accuracy on IMC-based machine learning accelerators. 

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5. SF-GRASS: solver-free graph spectral sparsification

   https://doi.org/10.1145/3400302.3415629  

   Zhang, Ying ; Zhao, Zhiqiang ; Feng, Zhuo ( November 2020 , International Conference on Computer-Aided Design)

   null (Ed.)

   Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sparse matrices, spectral partitioning of undirected graphs, vectorless verification of power/thermal grids, representation learning of large graphs, etc. However, prior spectral graph sparsification methods rely on fast Laplacian matrix solvers that are usually challenging to implement in practice. This work, for the first time, introduces a solver-free approach (SF-GRASS) for spectral graph sparsification by leveraging emerging spectral graph coarsening and graph signal processing (GSP) techniques. We introduce a local spectral embedding scheme for efficiently identifying spectrally-critical edges that are key to preserving graph spectral properties, such as the first few Laplacian eigenvalues and eigenvectors. Since the key kernel functions in SF-GRASS can be efficiently implemented using sparse-matrix-vector-multiplications (SpMVs), the proposed spectral approach is simple to implement and inherently parallel friendly. Our extensive experimental results show that the proposed method can produce a hierarchy of high-quality spectral sparsifiers in nearly-linear time for a variety of real-world, large-scale graphs and circuit networks when compared with prior state-of-the-art spectral methods. 

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