About Me

I am broadly interested in the areas of deep learning, hardware architecture, and computer networks.  I have worked on developing efficient deep neural network (DNN) algorithms and have designed hardware architectures for these DNNs. I am also interested in optimizing DNN inference (prediction) for edge devices in both standalone and distributed network contexts.


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Recent Publications

Accelerating DNN Training with Structured Data Gradient Pruning

B. McDanel, H. Dinh, J. Magallanes
International Conference on Pattern Recognition (ICPR), 2022.
preprint

the diagram shows how to use two separate squares
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

a diagram showing the different components of a computer system
Saturation RRAM Leveraging Bit-level Sparsity Resulting from Term Quantization

B. McDanel, H. T. Kung, S. Zhang
IEEE International Symposium on Circuits and Systems (ISCAS), 2021
paper

a diagram showing the different types of data
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 Operating Systems (ASPLOS), 2021
preprint

two diagrams showing the different types of hardware