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MCPS: a mapping method for MAERI accelerator base on Cartesian Product based Convolution for DNN layers with sparse input feature map

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Abstract

Today, the high accuracy of deep learning has led to use in various domains such as image and voice classification. However, vast computations of deep neural networks (DNNs) have caused the inefficiency of traditional processors, resulting in the emergence of hardware accelerators. DNN accelerators have increased performance by exploiting opportunities such as data reuse and sparsity. In these accelerators, the dataflow is an essential factor, so that some of them have a reconfigurable architecture to support different mappings and dataflows. However, the accelerators explicitly designed for exploiting the data sparsity are usually non-reconfigurable and have fixed dataflow. This paper presents a new dataflow called Channel Dimension Stationary (CDS) for the MAERI (a Reconfigurable Neural Network Accelerator). It can be used for convolutional layers with sparse input feature maps (ifmaps). In the proposed dataflow, computations are based on the Cartesian product method. However, multiplications leading to useless results are avoided. To analyze the mapping based on CDS dataflow, we upgraded the mRNA tool (mapper for Reconfigurable Neural Accelerators), which includes an energy and performance analyzer of the mapping strategy in MAERI. By evaluation, we found that in the sparse ifmaps of 50%, 70%, and 90%, the proposed mapping on average can increase energy efficiency by 3x, 6x, and 13x respectively, without noticeable reduction of utilization.

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Data availability

The original data and analyzer used and analyzed during the current study are available in the Github repository, https://github.com/georgia-tech-synergy-lab/mRNA. In addition, part of the data generated during the present study is included in this manuscript, and generated codes are available from the corresponding author on request.

Notes

  1. Input data (e.g., images) is transformed into a feature map by passing through CNN layers.

  2. ScratchPad Memory (Prefetch Buffer).

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Funding

This work has received no funding.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Babak Narimanjahan & Midia Reshadi

  2. School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

    Midia Reshadi

  3. Iran Telecommunication Research Center (ITRC), Tehran, Iran

    Ahmad Khademzadeh

  4. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Akram Reza

Authors
  1. Babak Narimanjahan
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  2. Midia Reshadi
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  3. Ahmad Khademzadeh
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  4. Akram Reza
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Contributions

Midia Reshadi introduced the subject and tools. Then, Babak NarimanJahan proposed the idea of the work and, by the approval of Midia Reshadi and Ahmad KhademZadeh, started analyzing, experimenting, and modifying mRNA based on the proposed idea. With the guidance of Midia Reshadi and the advice of Akram Reza, and the supervision of Ahmad KhademZadeh, Babak NarimanJahan analyzed the results of the experiments and wrote the manuscript. All authors read and approved the final version.

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Correspondence to Ahmad Khademzadeh.

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Narimanjahan, B., Reshadi, M., Khademzadeh, A. et al. MCPS: a mapping method for MAERI accelerator base on Cartesian Product based Convolution for DNN layers with sparse input feature map. Cluster Comput 25, 3213–3230 (2022). https://doi.org/10.1007/s10586-021-03527-6

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  • DOI: https://doi.org/10.1007/s10586-021-03527-6

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