Search Results for author:
Found 19 papers, 5 papers with code
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
In this paper, we propose MIMONet, a novel on-device multi-input multi-output (MIMO) DNN framework that achieves high accuracy and on-device efficiency in terms of critical performance metrics such as latency, energy, and memory usage.
Localised Adaptive Spatial-Temporal Graph Neural Network
Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies.
Subspace-Configurable Networks
The obtained subspace is low-dimensional and has a surprisingly simple structure even for complex, non-invertible transformations of the input, leading to an exceptionally high efficiency of subspace-configurable networks (SCNs) when limited storage and computing resources are at stake.
p-Meta: Towards On-device Deep Model Adaptation
Data collected by IoT devices are often private and have a large diversity across users.
Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks
In this paper, we propose Hyper Time- Series Forecasting (HTSF), a hypernetwork-based framework for accurate time series forecasting under distribution shift.
Robust Resource-Aware Self-triggered Model Predictive Control
The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life.
Memory-Aware Partitioning of Machine Learning Applications for Optimal Energy Use in Batteryless Systems
By leveraging interkernel data dependencies, these energy-bounded execution cycles minimize the number of system activations and nonvolatile data transfers, and thus the total energy overhead.
Using system context information to complement weakly labeled data
Real-world datasets collected with sensor networks often contain incomplete and uncertain labels as well as artefacts arising from the system environment.
Measuring what Really Matters: Optimizing Neural Networks for TinyML
With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity.
Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference
Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples.
The Time-Triggered Wireless Architecture
Wirelessly interconnected sensors, actuators, and controllers promise greater flexibility, lower installation and maintenance costs, and higher robustness in harsh conditions than wired solutions.
Networking and Internet Architecture
Adaptive Loss-aware Quantization for Multi-bit Networks
We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms.
Pruning-Aware Merging for Efficient Multitask Inference
Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost.
Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge
Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints.
Multi-Task Zipping via Layer-wise Neuron Sharing
Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device.
Unsupervised Feature Learning for Audio Analysis
It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction.
Efficient Convolutional Neural Network For Audio Event Detection
Wireless distributed systems as used in sensor networks, Internet-of-Things and cyber-physical systems, impose high requirements on resource efficiency.
EURETILE D7.3 - Dynamic DAL benchmark coding, measurements on MPI version of DPSNN-STDP (distributed plastic spiking neural net) and improvements to other DAL codes
The EURETILE project required the selection and coding of a set of dedicated benchmarks.
EURETILE 2010-2012 summary: first three years of activity of the European Reference Tiled Experiment
Furthermore, EURETILE investigates and implements the innovations for equipping the elementary HW tile with high-bandwidth, low-latency brain-like inter-tile communication emulating 3 levels of connection hierarchy, namely neural columns, cortical areas and cortex, and develops a dedicated cortical simulation benchmark: DPSNN-STDP (Distributed Polychronous Spiking Neural Net with synaptic Spiking Time Dependent Plasticity).
