Skip to content

Latest commit

 

History

History
102 lines (72 loc) · 4.37 KB

README.md

File metadata and controls

102 lines (72 loc) · 4.37 KB
Raw

PULP-NNX kernel libraries

A kernel library targeting Neural Network Accelerators developed in the PULP group.

Disclaimer

This library is considered unstable and might go through major changes until a stable release of v1.0.0.

NNX Interface

The interface to each accelerator consists of these functions:

void <accelerator>_nnx_init(<accelerator>_dev_t *dev, <accelerator>_pulp_conf_t *conf);
void <accelerator>_nnx_term(<accelerator>_dev_t *dev);
int <accelerator>_nnx_dispatch(<accelerator>_dev_t *dev, <accelerator>_task_t *task);
int <accelerator>_nnx_dispatch_check(<accelerator>_dev_t *dev);
void <accelerator>_nnx_dispatch_wait(<accelerator>_dev_t *dev);
int <accelerator>_nnx_resolve_check(<accelerator>_dev_t *dev, <accelerator>_task_t *task);
void <accelerator>_nnx_resolve_wait(<accelerator>_dev_t *dev, <accelerator>_task_t *task);

Each accelerator has their own named function in case there exist multiple types of accelerators on a same board.

Each function accepts a pointer to a <accelerator>_dev_t type which to discern between each accelerator.

Note: The accelerator can provide additional helper functions if needed.

Repository structure

  • inc: nnx interface for each accelerator
  • src: implementation for each accelerator
  • util: utilities used by all the accelerators
  • <accelerator>:
    • hal: hardware abstraction layer
    • gvsoc: gvsoc-specific functions
    • bsp: board support package for each board that has the accelerator
  • test: testing folder (more info)

Accelerators

Testing

You can find information about testing in the dedicated README.

Environment

The library was tested with following pairs of SDKs and compilers:

SDK SDK Commit Hash Compiler Compiler Commit Hash
gap_sdk (obtainable from GreenWaves Technologies) 90df4ce219 gap_gnu_toolchain 360fd4f9d6
pulp-sdk c216298881 pulp-riscv-gnu-toolchain 9938bd8fcf (release v1.0.16)

Contributing

Bug reports and feature requests should be reported through issues. All the development should be done through forks and merged onto the dev branch with pull requests.

Versioning

The library will follow the Semantic Versioning.

Publication

If you use PULP-NNX in your work, you can cite us: 
@inproceedings{10.1145/3607889.3609092,
    author = {Macan, Luka and Burrello, Alessio and Benini, Luca and Conti, Francesco},
    title = {WIP: Automatic DNN Deployment on Heterogeneous Platforms: the GAP9 Case Study},
    year = {2024},
    isbn = {9798400702907},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3607889.3609092},
    doi = {10.1145/3607889.3609092},
    abstract = {Emerging Artificial-Intelligence-enabled System-on-Chips (AI-SoCs) combine a flexible microcontroller with parallel Digital Signal Processors (DSP) and heterogeneous acceleration capabilities. In this Work-in-Progress paper, we focus on the GAP9 RISC-V SoC as a case study to show how the open-source DORY Deep Neural Network (DNN) tool flow can be extended for heterogeneous acceleration by fine grained interleaving of a dedicated Neural Engine and a cluster of RISC-V cores. Our results show that up to 91\% of the peak accelerator throughput can be extracted in end-to-end execution of benchmarks based on MobileNet-V1 and V2.},
    booktitle = {Proceedings of the International Conference on Compilers, Architecture, and Synthesis for Embedded Systems},
    pages = {9–10},
    numpages = {2},
    keywords = {TinyML, MCUs, deep learning, HW accelerators},
    location = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},
    series = {CASES '23 Companion}
}

Contributors

License

Licensed under Apache-2.0; the whole text of the license can be found in the LICENSE file.