Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications

Bavikadi, Sathwika; Sutradhar, Purab Ranjan; Ganguly, Amlan; Dinakarrao, Sai Manoj Pudukotai

doi:10.1109/vlsid60093.2024.00043

2024 37th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID) 2024

DOI: 10.1109/vlsid60093.2024.00043

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Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications

Sathwika Bavikadi,

Purab Ranjan Sutradhar,

Amlan Ganguly

et al.

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Cited by 2 publications

References 15 publications

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ReApprox-PIM: Reconfigurable Approximate Lookup-Table (LUT)-Based Processing-in-Memory (PIM) Machine Learning Accelerator

Bavikadi,

Sutradhar,

Indovina

et al. 2024

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

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ReApprox-PIM: Reconfigurable Approximate Lookup-Table (LUT)-Based Processing-in-Memory (PIM) Machine Learning Accelerator

Bavikadi,

Sutradhar,

Indovina

et al. 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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Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications

Cited by 2 publications

References 15 publications

ReApprox-PIM: Reconfigurable Approximate Lookup-Table (LUT)-Based Processing-in-Memory (PIM) Machine Learning Accelerator

ReApprox-PIM: Reconfigurable Approximate Lookup-Table (LUT)-Based Processing-in-Memory (PIM) Machine Learning Accelerator

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

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