39 citations · 47 across the 6 of their papers we have counts for
8 papers
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Lois Orosa, Skanda Koppula, Yaman Umuroglu +5
Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applicat…
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser +2
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications, spanning from image classification to speech recognition. While providing excellent accuracy,…
Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine
Prasanth Chatarasi, Stephen Neuendorffer, Samuel Bayliss +2
Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that includes novel support for 2D SIMD datapaths and shuffle interconnection network. The cur…
Efficient Error-Tolerant Quantized Neural Network Accelerators
Giulio Gambardella, Johannes Kappauf, Michaela Blott +4
Neural Networks are currently one of the most widely deployed machine learning algorithms. In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evalu…
Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels
Murad Qasaimeh, Kristof Denolf, Jack Lo +3
Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embe…
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Yifan Yang, Qijing Huang, Bichen Wu +8
Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a res…