Search Results for author:
Found 6 papers, 2 papers with code
Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution
Embedded and IoT devices, largely powered by microcontroller units (MCUs), could be made more intelligent by leveraging on-device deep learning.
Differentiable Network Pruning for Microcontrollers
Embedded and personal IoT devices are powered by microcontroller units (MCUs), whose extreme resource scarcity is a major obstacle for applications relying on on-device deep learning inference.
$μ$NAS: Constrained Neural Architecture Search for Microcontrollers
IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resources than is typically required for deep learning.
The Final Frontier: Deep Learning in Space
Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems.
Neural networks on microcontrollers: saving memory at inference via operator reordering
Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence.
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements.
