27 citations · 62 across the 6 of their papers we have counts for
6 papers
Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution
Edgar Liberis, Nicholas D. Lane
Embedded and IoT devices, largely powered by microcontroller units (MCUs), could be made more intelligent by leveraging on-device deep learning. One of the main challenges of neura…
Differentiable Network Pruning for Microcontrollers
Edgar Liberis, Nicholas D. Lane
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 learni…
NAS: Constrained Neural Architecture Search for Microcontrollers
Edgar Liberis, Łukasz Dudziak, Nicholas D. Lane
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 persist…
The Final Frontier: Deep Learning in Space
Vivek Kothari, Edgar Liberis, Nicholas D. Lane
Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a spac…
Neural networks on microcontrollers: saving memory at inference via operator reordering
Edgar Liberis, Nicholas D. Lane
Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for build…
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
Petar Veličković, Laurynas Karazija, Nicholas D. Lane +6
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objecti…