[ICML 2024 Oral] Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

Efficient LLM Inference Acceleration using Prompting

[ICML'24] Recurrent Early Exits for Federated Learning with Heterogeneous Clients

[WACV 2024] Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation

[NeurIPS'23] FedL2P: Federated Learning to Personalize

The codes and artifacts associated with our MICRO'22 paper titled: "Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design"

Open-source artifacts and codes of our MICRO'23 paper titled “Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads”.