1. CS 295: Modern Systems Lab2: Convolution Accelerators
Sang-Woo Jun
2019 Spring

2. Lab 2 Goals Implement a hardware accelerator for convolution
Ostensibly useful for the convolution layers of a neural network
Weight stationary design
Template and testbench framework written in Bluespec
Weight stationary convolution for a row in the convolution
Partial sum of a previous
activation row if any
Partial sum for stored for
next activation row, or
final sum

3. Intuitive Example of a Convolution: Sobel Edge Detection Filter
Simple convolution filter for emphasizing edges in an image
Two convolutions sweep over the input image
The output of the two filters are joined after convolutions are completed
Images from Wikipedia

4. Framework Details https://github.uci.edu/swjun/cs295_19s
Lab 2 files in sub-directory “convolution”
Two Bluespec files
Top.bsv : Testbench framework. No need to edit
Filter.bsv : Convolution filter implementation. You need to edit this
Three C++/h files
bdpi.cpp : Testbench framework that emulates data input/output
createbmp.cpp/h : Creates bmp files from edge-detected output
One data file
datain.bin : 64 32*32 images from the CIFAR-10 dataset

5. In Filter.bsv mkFilter exposing parameterized FilterIfc interface
input channels, filter count, filter width
setWeight fills in a multidimensional vector “weights”
setWeightDone is called after testbench enters all weights
put is called with a input data vector
get is called to retrieve convolved data

6. Things To Keep in Mind
put is called repeatedly with a vector of channels per pixel (R,G,B)
There is no delineation between images. All images are 32*32 pixels
It is first converted to greyscale by simply averaging RGB values (provided in code)
Need to add zero padding around the images so that the output is the same dimension as the input
For simplicity, just add zero pixels around the smaller convolved output image
For sobel filter: Input is 32*32*3 per image, and output should be (for sobel filter) 32*32*2 per image

7. Tips
Pixels streamed through N row buffers (where N is width of filter)
Can use mkBRAMFIFO’s
For Sobel filter, each PE box in Fig. 3 should ideally implement 6 PEs, for 3 columns per each row, for two filters

8. Development Environment
Server at “orthanc.ics.uci.edu”
Login details will be ed to you
Bluespec and licenses already installed
vim syntax files for Bluespec included with this document
Copy to respective directories in ~/.vim
Required environment variables in ~/setupbsv.sh
Already source’d in .bashrc

9. Building And Running “make” ./bsim/obj/bsim Output: “outXX.bmp”
32*32 pixel greyscale
Currently just greyscale of input image
Suggest saving them for comparison

10. Submission
Same as lab 1
Submit Filter.bsv, and a short write-up (1+ paragraph is fine) describing how you did it
Please to me with title [CS295] lab2 – ucinetid