RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

  • Authors:
    Mohammad Samragh (UC/San Diego), Mohsen Imani (UC/San Diego), Yeseong Kim (UC/San Diego), Saransh Gupta (UC/San Diego), Farinaz Koushanfar (UC/San Diego), Tajana Rosing (UC/San Diego)
    Publication ID:
    Publication Type:
    Received Date:
    Last Edit Date:
    2780.017 (University of Wisconsin/Madison)
    2780.019 (Pennsylvania State University)


Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-theart DNNs on current systems mostly relies on either general purpose processors, ASIC designs, or FPGA accelerators, all of which suffer from data movements due to the limited on chip memory and data transfer bandwidth. In this work, we propose a novel framework, called RAPIDNN, which processes all DNN operations within the memory to minimize the cost of data movement. To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i.e., multiplication, addition, activation functions, and pooling. The framework extracts representative operands of a DNN model, e.g., weights and input values, using clustering methods to optimize the model for in-memory processing. Then, it maps the extracted operands and their precomputed results into the accelerator memory blocks. At runtime, the accelerator identifies computation results based on efficient in-memory search capability which also provides tunability of approximation to further improve computation efficiency. Our evaluation shows that RAPIDNN achieves 68.4×, 49.5× energy efficiency improvement and 48.1×, 10.9× speedup as compared to ISAAC and PipeLayer, the state-of-the-art DNN accelerators, while ensuring less than 0.3% of quality loss.

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