76 citations · 218 across the 11 of their papers we have counts for
6 papers · 1 filter
StitchNet: Composing Neural Networks from Pre-Trained Fragments
Surat Teerapittayanon, Marcus Comiter, Brad McDanel +1
We propose StitchNet, a novel neural network creation paradigm that stitches together fragments (one or more consecutive network layers) from multiple pre-trained neural networks.…
Accelerating DNN Training with Structured Data Gradient Pruning
Bradley McDanel, Helia Dinh, John Magallanes
Weight pruning is a technique to make Deep Neural Network (DNN) inference more computationally efficient by reducing the number of model parameters over the course of training. How…
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Sai Qian Zhang, Bradley McDanel, H. T. Kung
Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network (DNN) training by providing a wide dynamic range via a shared exponent across a group of val…
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…
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…
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference
Bradley McDanel, Surat Teerapittayanon, H. T. Kung
We propose the use of incomplete dot products (IDP) to dynamically adjust the number of input channels used in each layer of a convolutional neural network during feedforward infer…