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Found 19 papers, 6 papers with code
Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC
We study various machine learning based algorithms for performing accurate jet flavor classification on field-programmable gate arrays and demonstrate how latency and resource consumption scale with the input size and choice of algorithm.
Deeper Hedging: A New Agent-based Model for Effective Deep Hedging
With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels.
When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA
Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving.
MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration
This paper introduces a novel optimization framework for deep neural network (DNN) hardware accelerators, enabling the rapid development of customized and automated design flows.
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance.
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
By jointly optimizing the algorithm and hardware, our FPGA-based butterfly accelerator achieves 14. 2 to 23. 2 times speedup over state-of-the-art accelerators normalized to the same computational budget.
Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model
It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation.
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach
We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios.
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs.
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
The proposed approach has been evaluated based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs.
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems.
An FPGA Accelerated Method for Training Feed-forward Neural Networks Using Alternating Direction Method of Multipliers and LSMR
In this project, we have successfully designed, implemented, deployed and tested a novel FPGA accelerated algorithm for neural network training.
An Analysis of Alternating Direction Method of Multipliers for Feed-forward Neural Networks
This is while the use of matrix inversion, which is challenging for hardware implementation, is avoided in this method.
Learning Absolute Sound Source Localisation With Limited Supervisions
By combining the unreliable innate response and the sparse reinforcement rewards, an accurate auditory space map, which is hard to be achieved by either one of these two kind of supervisions, can eventually be learned.
Optimizing CNN-based Hyperspectral ImageClassification on FPGAs
Hyperspectral image (HSI) classification has been widely adopted in applications involving remote sensing imagery analysis which require high classification accuracy and real-time processing speed.
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification
Hyperspectral images (HSIs) can distinguish materials with high number of spectral bands, which is widely adopted in remote sensing applications and benefits in high accuracy land cover classifications.
Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks.
Efficient Structured Pruning and Architecture Searching for Group Convolution
Efficient inference of Convolutional Neural Networks is a thriving topic recently.
