Fig. 7
From: HDBind: encoding of molecular structure with hyperdimensional binary representations
Processing latency measurements on the training (left) and testing (right) sets of LIT-PCBA. Times correspond to the GPU execution time per molecule measured using the PyTorch CUDA backend. (Left) Horizontal lines for MLP and HDBind correspond to the median time for training. The HDBind methods maintain similar training times for values of D up to approximately 100k. When considering the overhead for hyperparameter optimization for the MLP, HDBind demonstrates improved latency for all values of D considered in this study, including \(D=1,000,000\). (Right) Horizontal lines for MLP and HDBind correspond to the median time for training. The horizontal line for FPGA inference represents the mean time per molecule. The HDBind methods maintain similar testing times for values of D up to 100k. Inference on the FPGA for HDBind is over an order of magnitude faster than the MLP baseline.
