Search Results for author:
Found 9 papers, 5 papers with code
ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs
In this paper, we present ByteTransformer, a high-performance transformer boosted for variable-length inputs.
HAWQV3: Dyadic Neural Network Quantization
Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Recognition Oriented Iris Image Quality Assessment in the Feature Space
The quality metric proposed in this paper can significantly improve the performance of the recognition algorithm while reducing the number of images discarded for recognition, which is advantageous over hand-crafted factors based iris quality assessment methods.
An End-to-End Autofocus Camera for Iris on the Move
To accommodate users at different distances, it is necessary to control focus quickly and accurately.
Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
Extensive experiments demonstrate the advantages of the PAAS technique to counter diverse face spoofing attacks (print, replay, mask) in uncontrolled indoor and outdoor conditions by learning polarized face images of 33 people.
Fast Gunrock Subgraph Matching (GSM) on GPUs
In this paper, we propose a GPU-efficient subgraph isomorphism algorithm using the Gunrock graph analytic framework, GSM (Gunrock Subgraph Matching), to compute graph matching on GPUs.
Distributed, Parallel, and Cluster Computing
UNIT: Unifying Tensorized Instruction Compilation
However, it is hard to leverage mixed precision without hardware support because of the overhead of data casting.
Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance
Today's auto-tuners (e. g., AutoTVM, Ansor) generate efficient tensor programs by navigating a large search space to identify effective implementations, but they do so with opaque hardware details.
