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Applications

This subdirectory contains some applications or benchmarks specifically implemented and optimized for Snitch.

Contents

  • Data generation:
    • data_gen.py: script to generate data and expected results for various benchmarks
    • data: output folder of data_gen.py which also contains the configuration to generate the data
  • src:
    • kernels: basic kernels, currently contains GEMM, BatchNorm, Maxpool, Fusedconv
    • layers: wraps the kernel to form a DNN layer. Manages data-movement, synchronization, double buffering etc.
    • utils: some helpful functions for benchmarking, verification, fast memset
    • net_layer.c: various ready tests to run layers.
  • include: includes layer struct.

SW Testbenches

There are currently a few tests for various layer types. Some additional information about these tests is given below:

  • net_maxpool.c: Naive implementation of a maxpooling layer, not optimized in any way due to memory-boundness
  • net-batchnorm.c: Implementation of a batchnorm layer with SSR streams (both read and write)
  • net-conv2d.c: Implementation and tiling of a 2D convolution that can be distributed to multiple clusters. The convolution is implemented as an im2col transformation (performed by 2D DMA transfers) + optimized GEMM. The memory layout of input and output feature map is Height x Width x Channels. The convolution is globally parallelized over output channels. Inside a cluster, the output pixels are distributed among the cores. There is an option to load the feature map from a different cluster instead of the main memory by setting cluster2cluster in the layer struct to 1. Currently only fp64 is implemented, but the data movement for fp32 or lower precision SIMD should be analogously.
  • net-gemm.c: Testbench to benchmark the optimized GEMM implementation for different memory layouts, dimensions and precisions.
  • net-fusedconv.c: Implementation of a fused kernel with Conv2d + BatchNorm + ReLU. The interface of the kernel is compatible with DORY. Parameters of a tile can be specified in data/fusedconv_param.hjson. Supported paramters are input/output dimension, padding, kernel dimension & stride, flags for BatchNorm and ReLU. Further there are two additional specialized kernels 1) a CHW kernel for input layers with very few input channels, the output of this kernel is in the HWC layout again 2) A depthwise kernel

Usage

To run a specific benchmark, first configure the dimensions and the desired precision data/app_params.hjson. 

{
    kernel: "GEMM"
    M: 16,
    N: 16,
    K: 16,
    alpha: 0,
    transpose_A: false,
    transpose_B: true,
    prec: 16
}

The file will be automatically generated with a cmake macro and is stored in data/data_app.h. The result will also be checked. Reference is a golden model written in python with help of the torch.

The applications are compiled into a folder which can be enabled by adding add_subdirectory(${SNITCH_SOFTWARE_DIR}/applications to CMakeLists.txt in the specific sw folder.

Requirements

  • torch

Updated on 2023-06-19 at 09:43:56 +0000