Reproducible artifacts and codes of our MICRO'23 paper titled "Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads". This repo mainly contains: 1. Sparse Multi-DNN Benchmark: Collections of diverse sparsified (SparseML)) Convolutional NNs (CNNs) and Attention-based NNs (AttNNs) from three distinct applications, namely visual perception, personal assistant, and hand tracking. 2. Simulation-based Evaluation Infrastructure: Seamless integration with PyTorch to evaluate the performance of different multi-DNN scheduling approaches. 3. Prototype of our Static and Dynamic Scheduler: FPGA-based implementation to obtain resource estimation of hardware resources.

If you found it helpful, pls cite us using: @inproceedings{fan2023sparse-dysta, title={{Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads}}, author={Fan, Hongxiang and Venieris, Stylianos I. and Kouris, Alexandros and Lane, Nicholas D.}, booktitle={56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)}, year={2023} }