SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

Gogineni, Kailash; Dayapule, Sai Santosh; Gómez-Luna, Juan; Gogineni, Karthikeya; Wei, Peng; Lan, Tian; Sadrosadati, Mohammad; Mutlu, Onur; Venkataramani, Guru

doi:10.1109/ispass61541.2024.00029

2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) 2024

DOI: 10.1109/ispass61541.2024.00029

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SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

Kailash Gogineni,

Sai Santosh Dayapule,

Juan Gómez-Luna

et al.

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

Kaur,

Asad,

Mohammadi

2024

Computers

View full text Add to dashboard Cite

show abstract

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SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

Cited by 1 publication

References 80 publications

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

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

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