model.py implements a resource and latency model to support expert users during the customization of the accelerators available within Faber.
To use the model, please provide the following arguments:
- Similarity metric (available metrics: cross-correlation, mean-squared error, mutual information, normalized mutual information):
-m/--metric {cc, mse, mi, nmi} - Number of cores (optional; default: 1):
-n/--num_cores NUM_CORES - Numer of Processing Elements (PEs) (must be a power of 2):
-pe PROCESSING_ELEMENT - Input bitwidth (must be a power of 2):
-b/--input_bitwidth {8,16,32,64,128,256,512} - Input dimension (assuming a squared image DxD, just provide D):
-d/--input_dimension INPUT_DIMENSION - Caching (optional):
-c/--caching - URAM usage for caching (optional):
-u/--uram - Enable HW transform (optional):
-t/-transform - Interpolation to use if transform is enabled (available interpolations: nearest neighbors, bilinear; default: nearest neighbors):
-i/--interpolation {nn, bln} - Target platform (available platforms: Ultra96, ZCU104, Alveo u200):
-p/--platform {ultra96, zcu104, alveo_u200}
Let's consider the following configuration:
- Input image: 512x512 8-bit image
- Similarity metric: 2-core mutual information accelerator with 16 PEs
- URAM caching
- Target platform: Alveo u200
To estimate the resource usage and latency of this configuration, run the model as follows:
python3 model.py -m mi -n 2 -pe 16 -b 8 -d 512 -c -u -p alveo_u200