# 2D DCT-II algorithm hardware-software codesign for image compression in RISC-V satellite processor

Hugo Mårdbrink, 2024

## Introduction

This project aims to design and implement a 2D DCT-II algorithm in hardware and software for image compression in a RISC-V satellite processor.
There is currently a rise of RISC-V processors, [notably in the space industry](https://gaisler.com/index.php/products/processors/noel-v#DOC).
The 2D DCT-II algorithm transforms blocks of pixels into blocks of frequency coefficients. This compresses the image by removing spatial redundancy.
Thus, the algorithm is used in spacecraft to decrease the amount of image data that needs to be transmitted back to Earth.

## Goals

Since the environment in space is limited, the design needs to focus on an energy efficient design using a small hardware area.
This alters the focus of the codesign to prefer energy efficiency over throughput or execution time.
However, the aspect of fast execution times is still highly relevant and a good balance between the two needs to be explored.

## Method

### Development and evaluation

The software will be compiled and built in C using the GCC RISC-V compiler. For vectorisation, vector intrinsics for RVV will be used from a C header file.
For parallelisation, the (OpenMP library)[https://www.openmp.org/] will be used.
To test and evaluate the software implementation, it will run in the gem5 simulator. The hardware configuration is also done in configuration files for gem5.
The mock data for the images will be generated in C with nonsensical values. This does not matter since different values will not affect the run time.
When measuring the performance the sequential time of generating mock data and freeing the memory will be deducted for a true performance reflection.

### Building 

To run the build command for the software, the following base command is used:

```bash
riscv64-unknown-elf-gcc -march=rv64imafcv -mabi=lp64d main.c -o dct2d_riscv.out
```

The following flags will be used based on what functionality is needed:

- `-lm` for math library
- `-libomp` for OpenMP library
- `-O[level]` for different optimisation levels
- `-march=rv64imafcv` for the RISC-V ISA
- `-mabi=lp64d` for the RISC-V ABI

### Simulating
To simulate the software on different hardware configurations gem5 is used.
Gem5 allows for different hardware configurations to be tested using a python script. 
The python script for this project is tailored for this project specifically, thus, 5 parameters are custom to this project for ease of use:

- `--l1i` for the L1 instruction cache size
- `--l1d` for the L1 data cache size
- `--l2` for the L2 cache size
- `--vlen` for the vector length
- `--elen` for the element length

To run the simulation and output the result, the following command is used:

```bash
../gem5/build/RISCV/gem5.opt -d stats/ ./riscv_hw.py --l1i 16kB --l1d 64kB --l2 256kB --vlen 256 --elen 64
```

## Implementation

### Constants and definitions
Throughout the code, several constants and definitions are defined for ease to try different configurations. These are defined in the following way:
- `DCT_SIZE` is the size of the DCT block
- `TOTAL_DCT_BLOCKS` is the total amount of DCT blocks and, thus, the problem size.
- `NUM_THREADS` is the amount of threads to use for parallelisation.
- `element_t` is the data type of the elements in the DCT block.
- `real_t` is the data type of the real variables of the algorithm.

### Mock data generation

To start testing our algorithm we need a way to generate data for reliable performance results.
This will be done by allocating DCT-blocks heap memory and filling them with data.
It's important to actually generate all the data and not reuse the same matrices to get realistic cache hits and misses.
The memory allocation is done in the following way:

### Initial hardware configuration


```c
element_t ***mock_matrices = (element_t ***) malloc(TOTAL_DCT_BLOCKS * sizeof(element_t**));
for (int i = 0; i < TOTAL_DCT_BLOCKS; i++) {
  mock_matrices[i] = (element_t **) malloc(DCT_SIZE * sizeof(element_t*));
  for (int j = 0; j < DCT_SIZE; j++) {
    mock_matrices[i][j] = (element_t *) malloc(DCT_SIZE * sizeof(element_t));
  }
}
```

And the data is generated using the following code:

```c
for (long i = 0; i < TOTAL_DCT_BLOCKS; i++) {
    for (int j = 0; j < DCT_SIZE; j++) {
        for (int k = 0; k < DCT_SIZE; k++) {
            mock_matrices[i][j][k] = j + k;
        }
     }
  }
```

### Naive
The first iteration of this code (see below) will use a naive implementation of DCT-II, along with no optimisations. 

```c
void dct_2d(element_t** matrix_in, element_t** matrix_out) {
    real_t cu, cv, sum;
    int u, v, i, j;

    for (u = 0; u < DCT_SIZE; u++) {
        for (v = 0; v < DCT_SIZE; v++) {
            cu = u == 0 ? 1 / sqrt(DCT_SIZE) : sqrt(2) / sqrt(DCT_SIZE);
            cv = v == 0 ? 1 / sqrt(DCT_SIZE) : sqrt(2) / sqrt(DCT_SIZE); 

            sum = 0;
            for (i = 0; i < DCT_SIZE; i++) {
                for (j = 0; j < DCT_SIZE; j++) {
                    sum += matrix_in[i][j] * cos((2 * i + 1) * u * PI / (2 * DCT_SIZE)) * cos((2 * j + 1) * v * PI / (2 * DCT_SIZE));
                }
            }
            matrix_out[u][v] = cu * cv * sum;
        }
    }
}
```