arasi15 / CNN-Accelerator-Implementation-based-on-Eyerissv2

CNN Accelerator Implementation based on Eyerissv2

This is a course project based on Verilog.

A CNN accelerator architecture based on Eyeriss v2 is modeled to progress convolutional layers, full connected layers and depth-conv layers.

RTL

• GLB_CLUSTER
  • GLB_WGHT
  • GLB_IACT
  • GLB_PSUM

• PE_CLUSTER
• PE
  • SPad
  • MUX
  • MAC

• ROUTER_CLUSTER

  • ROUTER_IACT
  • ROUTER_WGHT
  • ROUTER_PSUM

• HMNOC

  • 4_CLUSTER

    • 1_CLUSTER

      

SIMULATION

• 1_CLUSTER
  • HMNOC_1_CLUSTER_TB

• 4_CLUSTER
  • HMNOC_4_CLUSTER_TB

Demo

Average Filter

Result

Reference

1. {Y.-H. Chen, J. Emer, and V. Sze, “Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks,” ISCA, 2016.}
2. {Y.-H. Chen, S. Member, T.-J. Yang, J. Emer, V. Sze, and S. Member, “Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices,” ISCA, 2019.}
3. https://github.com/karthisugumar/CSE240D-Hierarchical_Mesh_NoC-Eyeriss_v2