Search Results for author: Michaela Blott

Found 19 papers, 8 papers with code

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

1 code implementation28 Sep 2022 Zhiqiang Que, Marcus Loo, Hongxiang Fan, Michaela Blott, Maurizio Pierini, Alexander D Tapper, Wayne Luk

In addition, we introduce an outer-product based matrix multiplication approach which is enhanced by the strength reduction for low latency design.

Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems

no code implementations24 Jun 2022 Pedro J. Freire, Michael Anderson, Bernhard Spinnler, Thomas Bex, Jaroslaw E. Prilepsky, Tobias A. Eriksson, Nelson Costa, Wolfgang Schairer, Michaela Blott, Antonio Napoli, Sergei K. Turitsyn

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to that of a dispersion equalizer.

QONNX: Representing Arbitrary-Precision Quantized Neural Networks

1 code implementation15 Jun 2022 Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott, Jovan Mitrevski, Ben Hawks, Nhan Tran, Vladimir Loncar, Sioni Summers, Hendrik Borras, Jules Muhizi, Matthew Trahms, Shih-Chieh Hsu, Scott Hauck, Javier Duarte

We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks.

Quantization

EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators

no code implementations4 Feb 2022 Lois Orosa, Skanda Koppula, Yaman Umuroglu, Konstantinos Kanellopoulos, Juan Gomez-Luna, Michaela Blott, Kees Vissers, Onur Mutlu

We find that commonly-used low-power CNN inference accelerators based on spatial architectures are not optimized for both of these convolutional kernels.

Image Generation Image Segmentation +1

Applications and Techniques for Fast Machine Learning in Science

no code implementations25 Oct 2021 Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.

BIG-bench Machine Learning

FAT: Training Neural Networks for Reliable Inference Under Hardware Faults

no code implementations11 Nov 2020 Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Kees Vissers

Our experiments show that by injecting faults in the convolutional layers during training, highly accurate convolutional neural networks (CNNs) can be trained which exhibits much better error tolerance compared to the original.

Image Classification speech-recognition +1

LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications

no code implementations6 Apr 2020 Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser, Michaela Blott

Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficiencies in mapping the computation to hardware.

Network Intrusion Detection Quantization

Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA

no code implementations24 Mar 2020 Mairin Kroes, Lucian Petrica, Sorin Cotofana, Michaela Blott

We hybridize genetic algorithms and simulated annealing with traditional bin packing heuristics to create flexible mappers capable of grouping parameter memories such that each group optimally fits FPGA on-chip memories.

Efficient Error-Tolerant Quantized Neural Network Accelerators

no code implementations16 Dec 2019 Giulio Gambardella, Johannes Kappauf, Michaela Blott, Christoph Doehring, Martin Kumm, Peter Zipf, Kees Vissers

In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evaluated for deployment in safety critical applications such as self driving vehicles.

Quantization

QuTiBench: Benchmarking Neural Networks on Heterogeneous Hardware

1 code implementation11 Sep 2019 Michaela Blott, Lisa Halder, Miriam Leeser, Linda Doyle

In order to address these implementation challenges, a broad spectrum of new customized and heterogeneous hardware architectures have emerged, often accompanied with co-designed algorithms to extract maximum benefit out of the hardware.

Hardware Architecture

Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs

1 code implementation21 Nov 2018 Yifan Yang, Qijing Huang, Bichen Wu, Tianjun Zhang, Liang Ma, Giulio Gambardella, Michaela Blott, Luciano Lavagno, Kees Vissers, John Wawrzynek, Kurt Keutzer

DiracDeltaNet achieves competitive accuracy on ImageNet (88. 7\% top-5), but with 42$\times$ fewer parameters and 48$\times$ fewer OPs than VGG16.

FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks

no code implementations12 Sep 2018 Michaela Blott, Thomas Preusser, Nicholas Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu

Given a neural network description, the tool optimizes for given platforms, design targets and a specific precision.

Hardware Architecture

FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs

1 code implementation11 Jul 2018 Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar, Giulio Gambardella, Norbert Wehn, Michaela Blott

In this paper, we present the first systematic exploration of this design space as a function of precision for Bidirectional Long Short-Term Memory (BiLSTM) neural network.

Optical Character Recognition Quantization

SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks

1 code implementation CVPR 2018 Julian Faraone, Nicholas Fraser, Michaela Blott, Philip H. W. Leong

An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook.

Quantization

Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices

no code implementations21 Jun 2018 Thomas B. Preußer, Giulio Gambardella, Nicholas Fraser, Michaela Blott

Neural networks have established as a generic and powerful means to approach challenging problems such as image classification, object detection or decision making.

Decision Making Image Classification +3

Scaling Binarized Neural Networks on Reconfigurable Logic

no code implementations12 Jan 2017 Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost.

General Classification

FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

3 code implementations1 Dec 2016 Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values.

General Classification

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