(Bin Li, U. Wisconsin-Madison, presenting on 12/9/20 at 11:00 a.m. and 7:00 p.m. ET)
We propose a novel multiple instance learning (MIL) model for disease detection in whole slide images (WSI) as a driving application for developing hardware acceleration architectures. Our model applies self-attention mechanism on latent representations to model the dependencies between the instances. Our model demonstrates the state-of-the-art performance on both classic MIL benchmark datasets and real-word clinical WSI datasets. Collaborating with hardware research groups, we propose a processing-in-memory (PIM) design for our application as well as general long-sequence attention-based models, which outperforms other PIM-based and GPU-based baselines by a large margin. Meanwhile, we are continuously working with PIM, GPU, and FPGA groups on software-hardware co-design and acceleration performance benchmarking.
