This is a Eyeriss chip simulator that does the same thing as Eyeriss. It was inspired by EyerissF.
However, EyerissF is not a full simulator as it does integrate full Eyeriss mapping strategy into consideration. Also, it does not test a full images but one picture. If you try with the complete minist dataset, EyerissF is not working.
This repo reimplemented and reorganized all functions of EyerissF.
Eyeriss is a row stationary DNN accelerator.
If this is the first time you heard about Eyeriss, or you are not very familiar with Eyeriss, the readme of EyerissF gives a good explanation. Please refer to that.
Run Lenet5_Hive.py, it outputs inference results of LeNet5 on Mnist dataset. The first there convolution layer are compared with pytorch implementation. Please make sure you have pytorch and numpy installed.
- Configuration
- conf.py ( default Eyeriss config features)
- Eyeriss Chip
- PE.py ( row stationary processing element)
- EyerissF.py (containing 168 PEs; mapping a Pass of [Weight, IfMap] to each PE)
- Hive
- Conv2d
- FullyConnect
- Activiation
- Pooling
- Pre/PostProcess ( Compress and Decompress)
- LeNet5 on Hive.py (test LeNet5 on Mnist, output inference result)
- test IO2.py (test compression and decompression)