76 citations · 312 across the 18 of their papers we have counts for
18 papers
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs
H. T. Kung, Bradley McDanel, Sai Qian Zhang
We present a novel technique, called Term Revealing (TR), for furthering quantization at run time for improved performance of Deep Neural Networks (DNNs) already quantized with con…
DaiMoN: A Decentralized Artificial Intelligence Model Network
Surat Teerapittayanon, H. T. Kung
We introduce DaiMoN, a decentralized artificial intelligence model network, which incentivizes peer collaboration in improving the accuracy of machine learning models for a given c…
CheckNet: Secure Inference on Untrusted Devices
Marcus Comiter, Surat Teerapittayanon, H. T. Kung
We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integ…
Full-stack Optimization for Accelerating CNNs with FPGA Validation
Bradley McDanel, Sai Qian Zhang, H. T. Kung +1
We present a full-stack optimization framework for accelerating inference of CNNs (Convolutional Neural Networks) and validate the approach with field-programmable gate arrays (FPG…
Adversarial Learning of Semantic Relevance in Text to Image Synthesis
Miriam Cha, Youngjune L. Gwon, H. T. Kung
We describe a new approach that improves the training of generative adversarial nets (GANs) for synthesizing diverse images from a text input. Our approach is based on the conditio…
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization
H. T. Kung, Bradley McDanel, Sai Qian Zhang
This paper describes a novel approach of packing sparse convolutional neural networks for their efficient systolic array implementations. By combining subsets of columns in the ori…