Stylianos I. Venieris

Orcid: 0000-0001-5181-6251

According to our database1, Stylianos I. Venieris authored at least 57 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Online presence:

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Bibliography

2024
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads.
ACM Trans. Embed. Comput. Syst., July, 2024

NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution.
IEEE Trans. Mob. Comput., March, 2024

The Future of Consumer Edge-AI Computing.
IEEE Pervasive Comput., 2024

Progressive Mixed-Precision Decoding for Efficient LLM Inference.
CoRR, 2024

Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference.
CoRR, 2024

Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation.
ACM Trans. Design Autom. Electr. Syst., November, 2023

Multiple-Deep Neural Network Accelerators for Next-Generation Artificial Intelligence Systems.
Computer, March, 2023

TinyTrain: Deep Neural Network Training at the Extreme Edge.
CoRR, 2023

LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms.
Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems, 2023

Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads.
Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture, 2023

Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices.
Proceedings of the IEEE Symposium on Computers and Communications, 2023

MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge.
Proceedings of the IEEE Symposium on Computers and Communications, 2023

2022
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device.
ACM Trans. Embed. Comput. Syst., November, 2022

Guest Editorial: Bridging the Gap Between Industry and Academia for Networking Research.
IEEE Netw., 2022

Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions.
ACM Comput. Surv., 2022

Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs.
CoRR, 2022

Multi-DNN Accelerators for Next-Generation AI Systems.
CoRR, 2022

Adaptable mobile vision systems through multi-exit neural networks.
Proceedings of the MobiSys '22: The 20th Annual International Conference on Mobile Systems, Applications and Services, Portland, Oregon, 27 June 2022, 2022

Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design.
Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture, 2022

Multi-Exit Semantic Segmentation Networks.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation.
Proceedings of the HotMobile '21: The 22nd International Workshop on Mobile Computing Systems and Applications, 2021

OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices.
Proceedings of the IEEE International Conference on Smart Computing, 2021

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation.
Proceedings of the 29th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2021

How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures.
Proceedings of the 32nd IEEE International Conference on Application-specific Systems, 2021

2020
Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices.
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2020

Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars.
IEEE Consumer Electron. Mag., 2020

SPINN: synergistic progressive inference of neural networks over device and cloud.
Proceedings of the MobiCom '20: The 26th Annual International Conference on Mobile Computing and Networking, 2020

Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs.
Proceedings of the 37th International Conference on Machine Learning, 2020

HAPI: Hardware-Aware Progressive Inference.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2020

Caffe Barista: Brewing Caffe with FPGAs in the Training Loop.
Proceedings of the 30th International Conference on Field-Programmable Logic and Applications, 2020

Journey Towards Tiny Perceptual Super-Resolution.
Proceedings of the Computer Vision - ECCV 2020, 2020

A Throughput-Latency Co-Optimised Cascade of Convolutional Neural Network Classifiers.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions.
Proceedings of the DistributedML@CoNEXT 2020: Proceedings of the 1st Workshop on Distributed Machine Learning, 2020

2019
Automated methodologies for mapping convolutional neural networks on reconfigurable hardware.
PhD thesis, 2019

fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs.
IEEE Trans. Neural Networks Learn. Syst., 2019

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices.
CoRR, 2019

Poster: MobiSR - Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors.
Proceedings of the 25th Annual International Conference on Mobile Computing and Networking, 2019

MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors.
Proceedings of the 25th Annual International Conference on Mobile Computing and Networking, 2019

Towards Efficient On-Board Deployment of DNNs on Intelligent Autonomous Systems.
Proceedings of the 2019 IEEE Computer Society Annual Symposium on VLSI, 2019

Power-Aware FPGA Mapping of Convolutional Neural Networks.
Proceedings of the International Conference on Field-Programmable Technology, 2019

2018
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions.
ACM Comput. Surv., 2018

CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks.
CoRR, 2018

Deploying Deep Neural Networks in the Embedded Space.
CoRR, 2018

f-CNN<sup>x</sup>: A Toolflow for Mapping Multiple Convolutional Neural Networks on FPGAs.
CoRR, 2018

CascadeCNN: Pushing the performance limits of quantisation.
CoRR, 2018

f-CNNx: A Toolflow for Mapping Multiple Convolutional Neural Networks on FPGAs.
Proceedings of the 28th International Conference on Field Programmable Logic and Applications, 2018

Cascade^CNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks.
Proceedings of the 28th International Conference on Field Programmable Logic and Applications, 2018

DroNet: Efficient convolutional neural network detector for real-time UAV applications.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018

Approximate FPGA-Based LSTMs Under Computation Time Constraints.
Proceedings of the Applied Reconfigurable Computing. Architectures, Tools, and Applications, 2018

2017
fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs.
CoRR, 2017

Latency-driven design for FPGA-based convolutional neural networks.
Proceedings of the 27th International Conference on Field Programmable Logic and Applications, 2017

fpgaConvNet: Automated Mapping of Convolutional Neural Networks on FPGAs (Abstract Only).
Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017

2016
fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs.
Proceedings of the 24th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2016

2015
Towards heterogeneous solvers for large-scale linear systems.
Proceedings of the 25th International Conference on Field Programmable Logic and Applications, 2015


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