Projects
Neuromorphic Context-Aware Sensing for Energy-Efficient Mobile AI
The Brain in Your Pocket
Neuromorphic Context-Aware Sensing for Energy-Efficient Mobile AI - by Yusuf Bhula
Results: 80% energy saved
Neuromorphic Context-Aware Sensing for Energy-Efficient Mobile AI - by Yusuf Bhula
System architecture pipeline
Neuromorphic Context-Aware Sensing for Energy-Efficient Mobile AI - by Yusuf Bhula
Energy & battery results
Neuromorphic Context-Aware Sensing for Energy-Efficient Mobile AI - by Yusuf Bhula
Method, stack & dataset
I am Yusuf Bhula, a final-year BSc Computer Science student at the University of Staffordshire London. My interests sit at the intersection of mobile systems, machine learning, and energy-aware computing, and I enjoy building things that work end-to-end rather than just on paper.
Always-on mobile AI burns through batteries within hours. This project integrates two energy savings previously studied only in isolation, context-aware sensor gating and neuromorphic SNN inference, into one simulated pipeline. Evaluated on UCI HAR across 2,947 test windows, the combined approach cut energy use by 80% while accuracy fell only 1.7 percentage points.
https://uk.linkedin.com/in/yusuf-bhula-27a187220
Type of employment sought
Seeking graduate software engineering roles with a focus on machine learning, mobile/edge AI, energy-aware systems, or full-stack web development.
Areas available to work
London and the South-East UK; open to hybrid and fully remote roles across the UK; would consider relocation for the right opportunity.
Contact Yusuf