StreamPIM: Streaming Matrix Computation in Racetrack Memory

An, Yuda; Tang, Yunxiao; Yi, Shushu; Peng, Li; Pan, Xiurui; Sun, Guangyu; Luo, Zhaochu; Li, Qiao; Zhang, Jie

doi:10.1109/hpca57654.2024.00031

2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA) 2024

DOI: 10.1109/hpca57654.2024.00031

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StreamPIM: Streaming Matrix Computation in Racetrack Memory

Yuda An,

Yunxiao Tang,

Shushu Yi

et al.

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2024

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Cited by 3 publications

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Advanced Integration-Inspired Process-in-Memory: A Comprehensive Review of Design, Challenges, and Future Prospects

Cheng,

Guan,

et al. 2024

IEEE Access

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Advanced Integration-Inspired Process-in-Memory: A Comprehensive Review of Design, Challenges, and Future Prospects

Cheng,

Guan,

et al. 2024

IEEE Access

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PipePIM: Maximizing Computing Unit Utilization in ML-Oriented Digital PIM by Pipelining and Dual Buffering

Jeong,

Chung

2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks

Kaur,

Asad,

Mohammadi

2024

Computers

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This comprehensive review explores the advancements in processing-in-memory (PIM) techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses the challenges of monolithic chip architectures and highlights the benefits of chiplet-based designs in terms of scalability and flexibility. This review emphasizes dataflow-awareness, communication optimization, and thermal considerations in PIM-enabled manycore architectures. It discusses tailored dataflow requirements for different machine learning workloads and presents a heterogeneous PIM system for energy-efficient neural network training. Additionally, it explores thermally efficient dataflow-aware monolithic 3D (M3D) NoC architectures for accelerating CNN inferencing. Overall, this review provides valuable insights into the development and evaluation of chiplet and PIM architectures, emphasizing improved performance, energy efficiency, and inference accuracy in deep learning applications.

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StreamPIM: Streaming Matrix Computation in Racetrack Memory

Cited by 3 publications

References 63 publications

Advanced Integration-Inspired Process-in-Memory: A Comprehensive Review of Design, Challenges, and Future Prospects

Advanced Integration-Inspired Process-in-Memory: A Comprehensive Review of Design, Challenges, and Future Prospects

PipePIM: Maximizing Computing Unit Utilization in ML-Oriented Digital PIM by Pipelining and Dual Buffering

A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks

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