| Monday September 19 |
| 08:00-08:30 |
Breakfast (Registration and Poster setup) |
| 08:30-09:30 |
Diana Marculescu |
Keynote: "When Sustainability Meets Machine Learning: Efficient Learning from Cloud to Edge" |
|
| 09:30-10:00 |
James Clark |
What is lost when networks are compressed? |
| 10:00-10:30 |
Coffee Break and Posters |
| 10:30-11:00 |
Vahid Partovi Nia |
Edge implementation of deep models |
|
| 11:00-11:30 |
Mark Coates |
Efficient Bayesian Network Architecture Search for Graph Neural Networks |
| 11:30-12:00 |
Ehsan Saboori |
Running 2 bit quantized CNN models on ARM CPUs |
| 12:00-13:00 |
Lunch |
| 13:00-15:00 |
Evgeni Gousev |
Keynote: "tinyML: ultimate energy efficient machine learning solution for edgeAI" |
|
| 14:00-14:30 |
Muthucumaru Maheswaran |
JAMScript: A Programming Language for Edge Oriented Mobile Internet of Things |
| 14:30-15:00 |
Shahrokh Valaee |
Cooperative Location Estimation using Federated Learning |
| 15:00-15:30 |
Coffee Break and Posters |
| 15:30-16:00 |
Rachel E. Bouserhal |
Hearables and their potential as a tool for early disease detection |
|
| 16:00-16:30 |
Dounia Lakhmiri |
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization |
| 16:30-17:00 |
Masoud Asgharian |
Causal Discovery, Independence of Mechanism and Input Assumption and Selection Bias |
| 17:00-17:30 |
Michael Rabbat |
Asynchronous Federated Learning at Scale |
| Tuesday September 20 |
| 08:00-08:30 |
Breakfast (Registration and Poster setup) |
| 08:30-09:30 |
Wen Tong |
Keynote: Machine Learning Based Post-Shannon Cognition Communications |
|
| 09:30-10:00 |
Brett Meyer |
Transforming Intelligence for the Edge:
Challenges and Opportunities in Modeling, Optimization, and Deployment |
| 10:00-10:30 |
Coffee Break and Posters |
| 10:30-11:00 |
Yunaho Yu |
Challenges for Edge Device Machine Learning Platform |
|
| 11:00-11:30 |
Naoya Onizawa |
Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing |
| 11:30-12:00 |
Ghouthi Boukli Hacene |
DNN Quantization and acceleration for training and inference |
| 12:00-13:00 |
Lunch |
| 13:00-14:00 |
Song Han |
Keynote: Efficient AI Computing with Sparsity |
|
| 14:00-14:30 |
Christophe Dubach |
Very High-Level Synthesis of Neural Networks Accelerators for FPGAs |
| 14:30-15:00 |
Francois Leduc-Primeau |
Building Energy-Efficient AI Chips by Exploiting Energy-Reliability Tradeoffs |
| 15:00-15:30 |
Coffee Break and Posters |
| 15:30-16:00 |
Yvon Savaria |
Applications of Edge Intelligence, Applications, Lessons Learned and Platforms |
|
| 16:00-16:30 |
Andreas Moshovos |
Boosting Machine Learning Innovation: Computing Systems that Learn and Adapt |
| 16:30-17:00 |
Pascal Poupart |
Uncertainty Aware Federated Learning |
| 17:00-17:30 |
Sarath Chandar |
TBD |
| Board Number |
Poster Title |
| Monday September 19 |
| 1 |
Weighted Group L0-norm Constraint for Sparse Training |
| 2 |
NAS plus Pipeline for High Throughput Edge Inference BERT |
| 3 |
Generalizing ProxConnect on Vision Transformer Binarization |
| 4 |
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for ''In the Wild'' Edge Applications |
| 5 |
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks |
| 6 |
A Decomposition Method Supporting Many Factorization Structures |
| 7 |
Retention of Domain Adaptability in Compressed Neural Networks |
| 8 |
Sharpness-Aware Training for Accurate Inference on Noisy DNN Accelerators |
| 9 |
On the Importance of Integrating Curriculum Design for Teacher Assistant-based Knowledge Distillation |
| 10 |
Towards Finding Efficient Students via Blockwise Neural Architecture Search and Knowledge Distillation |
| 11 |
Quasi-convex floating points optmization |
| 12 |
Standard Deviation-Based Quantization for Deep Neural Networks |
| 13 |
S^3 Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks |
| 14 |
Inspecting the Role of Pretrained Transformers in Federated Learning |
| 15 |
Quantized One-dimensional Stacked CNN for Seizure Forecasting with Wearables |
| 16 |
BERT Inference Energy Predictor for Efficient Hardware-aware NAS |
| 17 |
Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition |
| 18 |
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization |
| Tuesday September 20 |
| 1 |
Limited-Memory Stochastic Partitioned Quasi-Newton Training |
| 2 |
A Short Study on Compressing Decoder-Based Language Models |
| 3 |
Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers |
| 4 |
Quadratic Regularization Optimizer in Low Precision for Deep Neural Networks: Implementation and Numerical Experience |
| 5 |
Gradient Distribution Theory for Exploding and Vanishing Gradient Problem |
| 6 |
Mixed representation integer fine-tuning of transformer-based models |
| 7 |
How Robust is Robust wav2vec 2.0 for Edge Applications?: An Exploration into the Effects of Quantization and Model Pruning on “In-the-Wild” Speech Recognition |
| 8 |
ARMCL BERT: Novel Quantizable BERT Implementation for ARM SoCs |
| 9 |
Kronecker Decomposition for GPT Compression |
| 10 |
Dyadic Integer Only BERT |
| 11 |
Learning Gaussian Restricted Boltzmann Machine using tensorial decompositions |
| 12 |
Persona Controlled Dialogue Prompting |
| 13 |
Toward Training Neural Networks with a Multi-Precision Quadratic Regularization Algorithm |
| 14 |
iRNN: Integer-only Recurrent Neural Network |
| 15 |
Latency and Accuracy Predictors for Efficient BERT Hardware-aware NAS |
| 16 |
Rational SoftMax |
| 17 |
Partially-Random Initialization: A Smoking Gun for Binarization Hypothesis of BERT |