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ConvLIB

ConvLib is a library of multi-threaded routines that offers a collection of efficient parallel convolution operators for multicore processors with ARM (NEON) or RISC-V architecture. Two major features of ConvLIB, which makes it different from other packages, is that 1) the routines in the library self-adapt to the cache hierarchy via an analytical model and 2) hardware-specific micro-kernels are generated automatically.

Requisites

OpenMP if a parallel execution is required.

Supported Hardware

Any processor with ARM NEON or RISC-V RVV 1.0 architecture. Tested on:

  • ARM A57, A78AE, CARMEL
  • RISC-V Xuantie C906, C910

How to install

  1. Modify the Makefile.inc file for configuring the installation.
  2. Configure the micro-kernel generation process in SIMD_generator.config file.
  3. Run the build.sh script seleccting the architecture type as follows: 
    $ ./build.sh armv8

How to use

  1. Configure the convolution features in the convolution.config file.
  2. Run the convolution.sh script as follows: 
    $ ./convolution.sh cnn/MODEL output/OUT

Where MODEL is the desired CNN model and OUT is the name of the output file.

Adding new CNN model

Adding a new CNN model is as easy as adding a new file to the cnn folder following the format of already existing ones.

Adding new hardware

  1. Add a new file in the cache-arch folder following the cache-TEMPLATE file.
  2. Add a new file in the SIMD-arch folder following the SIMD-TEMPLATE file.

How to cite

Parallel GEMM-based Convolutions for Deep Learning on Multicore ARM and RISC-V Architectures. Héctor Martínez, Sandra Catalán, Adrián Castelló, and Enrique S. Quintana-Ortí. J. of Systems Architecture, 2023. Under review. Available at SSRN: https://ssrn.com/abstract=4676008 or http://dx.doi.org/10.2139/ssrn.4676008

Related work

Micro-kernels for portable and efficient matrix multiplication in deep learning. Guillermo Alajeos, Adrián Castelló, Héctor Martínez, Pedro Alonso-Jordá, Francisco D. Igual, Enrique S. Quintana-Ortí. J. Supercomputing 79, 8124–8147 (2023). https://doi.org/10.1007/s11227-022-05003-3

AlgorithmXXX: Automatic Generators for a Family of Matrix Multiplication Routines with Apache TVM. Guillermo Alajeos, Adrián Castelló, Francisco D. Igual, Héctor Martínez, Enrique S. Quintana-Ortí. ACM Transactions on Mathematical Software, 2024. To appear. arXiv:2310.20347v1, 2023. https://arxiv.org/pdf/2310.20347.pdf

Funding

This work was supported by the research project PID2020-113656RB-C22 of MCIN/AEI/10.13039/501100011033. The project has also received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558 (eFlows4HPC project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme, and Spain, Germany, France, Italy, Poland, Switzerland, and Norway.