39 citations · 45 across the 6 of their papers we have counts for
16 papers
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA
Lucian Petrica, Tobias Alonso, Mairin Kroes +3
Custom dataflow Convolutional Neural Network (CNN) inference accelerators on FPGA are tailored to a specific CNN topology and store parameters in On-Chip Memory (OCM), resulting in…
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,…
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications
Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser +1
Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficie…
Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA
Mairin Kroes, Lucian Petrica, Sorin Cotofana +1
Convolutional neural network (CNN) dataflow inference accelerators implemented in Field Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower lat…
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…
Real-Time Machine Learning Based Fiber-Induced Nonlinearity Compensation in Energy-Efficient Coherent Optical Networks
Elias Giacoumidis, Yi Lin, Michaela Blott +1
We experimentally demonstrate the first field-programmable gate-array-based real-time fiber nonlinearity compensator (NLC) using sparse K-means++ machine learning clustering in an…