Speculative Decoding and Beyond: An In-Depth Review of Techniques
Yunhai Hu, Zining Liu, Zhenyuan Dong, Tianfan Peng, Bradley McDanel, and Sai Qian Zhang EMNLP Findings, 2025. preprint
PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding
Bradley McDanel, Sai Qian Zhang, Yunhai Hu, Zining Liu ACL Findings, 2025. preprint code
AMUSD: Asynchronous Multi-Device Speculative Decoding for LLM Acceleration
Bradley McDanel IEEE International Symposium on Circuits and Systems (ISCAS), 2025. preprint paper code
StitchNet: Composing Neural Networks from Pre-Trained Fragments
Surat Teerapittayanon, Marcus Comiter, Bradley McDanel, H. T. Kung. IEEE International Conference on Machine Learning (ICMLA), 2023. preprint
Accelerating Vision Transformer Training via a Patch Sampling Schedule
Bradley McDanel, Chi Phuong Huynh IEEE International Conference on Machine Learning (ICMLA), 2023. preprint code
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
S. Zhang, B. McDanel, H. T. Kung 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA-28), 2022. preprint
Field-Configurable Multi-resolution Inference: Rethinking Quantization
S. Zhang, B. McDanel, H. T. Kung, X. Dong 26th ACM International Conference on Architectural Support for Programming Languages and […]
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference
B. McDanel, S. Teerapittayanon, H. T. Kung International Conference On Machine Learning And Applications (ICMLA), 2017 paper
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
S. Teerapittayanon, B. McDanel, H. T. Kung International Conference on Distributed Computing Systems (ICDCS), 2017 paper | code
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
S. Teerapittayanon, B. McDanel, H. T. Kung International Conference on Pattern Recognition (ICPR), 2016 paper
