SOMA

This repository contains the code for "SoMa: Identifying, Exploring, and Understanding the DRAM Communication Scheduling Space for DNN Accelerators". Please follow the instructions in the AE Appendix of our corresponding HPCA 2025 paper.

Quick Start

1. First you will need to install some python libs: pip install -r requirements.txt or conda install --file requirements.txt
2. Optional: we use OpenMP to do multi-threaded search, if you do not want this, just comment out -fopenmp in the Makefile.

./build.sh
./run.sh --eta
./get_results.sh

Console Arguments Explanation

After running build.sh, you can execute a single experiment using the command:

./build/soma 108 2 512 1 8 1 8 4 256 512 849779186 results/dse

Argument Breakdown:

1. Network (108): Specifies the neural network to be used. (Full list below)
2. Baseline Type (2): Must be 2, as other values are not supported.
3. Sequence Length (512): Relevant for LLMs; ignored for CNNs.
4. Number of Segments (1): Used when a network is too large and needs partitioning for scheduling.
5. L2 Buffer Size (8 MB): Defines the L2 buffer size in megabytes.
6. Batch Size (1): Specifies the number of input samples processed at once.
7. DRAM Bandwidth Ratio (8): Ratio of DRAM bandwidth (GB/s) to computational power (TOPS). Default is 1.
8. PE Array Dimension (4): Typically ranges from 4 to 16.
9. L2 Buffer Bandwidth (256 GB/s): Specifies the bandwidth for L2 buffer.
10. MAC Units per PE (512): Determines the number of multiply-accumulate (MAC) units per PE. TOPS is calculated as:

    TOPS = 2 * mac_num * PE_ARRAY_Dim^2 / 1024
    

11. Random Seed (849779186): Used for the random number generator.
12. Results Folder (results/dse): Specifies where the experiment results are stored.

Supported Networks

Convolutional Neural Networks (CNNs):

• 0: Darknet19
• 1: VGG19
• 2: ResNet50
• 3: GoogLeNet
• 4: ResNet101
• 5: DenseNet
• 6: Inception-ResNet-V1
• 7: GNMT
• 8: LSTM
• 9: ZFNet
• 10: Transformer
• 11: Transformer Cell
• 12: PNASNet
• 13: ResNeXt50
• 14: ResNet152
• 15: Transformer Big Cell
• 16: RetinaNet-ResNet50
• 17: U-Net
• 18: RandWire Small
• 19: RandWire Large

Large Language Models (LLMs):

• 101: GPT-J 6B (Decode)
• 102: GPT-J 6B (Prefill)
• 103: LLaMa 2 70B (Decode)
• 104: LLaMa 2 70B (Prefill)
• 105: BERT Base
• 106: BERT Large
• 107: GPT-2 Small (Decode)
• 108: GPT-2 Small (Prefill)
• 109: GPT-2 XL (Decode)
• 110: GPT-2 XL (Prefill)

For unsupported models, the program will throw an error: Model not supported.

Folder Overview

include/

Contains header files for the project.

pyscripts/

Holds Python scripts used for processing and analyzing experiment results.

src/

Contains the source code (C++) for implementing the SoMa Framework.

Note on GPT: Please note that for GPT-2, we actually explored only one block, so in the data processing script, the corresponding latency is multiplied by the number of blocks. For GPT-2-small in the DSE experiments, the number of blocks is 12.

Contact Info

If you encounter any issues during the AE process, please contact:

Jingwei Cai (Tsinghua University) 1148821791@qq.com
Xuan Wang (Xi'an Jiaotong University, Institute for Interdisciplinary Information Core Technology) wangxuanxjtu@163.com