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19992022

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Personal profile

Biography

Prof Jim Harkin is Head of the School of Computing, Engineering and Intelligent Systems. He is a member of the Computational Neuroscience and Neuromorphic Engineering team in the Intelligent Systems Research Centre (ISRC) and  previously worked as a post-doc researcher in embedded systems design at Ulster (2001-2004) on the EPSRC funded project (GR/N26753). His current teaching interests relate to embedded systems encompassing digital systems design, VHDL for FPGAs and system testing. Dr. Harkin leads research on the design of intelligent embedded systems and focuses on developing electronic computing systems that can self-repair under the presence of errors. In particular, his work explores how computer models of neural networks (brain like-repair) can be mapped to hardware to build highly efficient and reliable embedded computers. His work has seen innovations in Networks-on-Chip strategies and the hardware implementation of self-repairing Spiking Neural Networks, and is Co-founder of the medical analaytics start-up company Respiratory Analytics. He has secured grant funding of +£3.5 million from several sources including EPSRC, Innovate UK, HSC R&D, MRC, InvestNI and DEL and has published over +140 articles in peer-reviewed journals and conferences. He was included in the University’s Computer Science (UoA 23) submission to the RAE 2008, REF2014 and also UoA 11 in REF2021.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 4 - Quality Education
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 11 - Sustainable Cities and Communities
  • SDG 12 - Responsible Consumption and Production

Keywords

  • TK Electrical engineering. Electronics Nuclear engineering
  • Neural network Hardware Systems
  • Networks-on-Chip
  • Fault tolerance
  • Self-adapting strategies for electronic systems
  • Reliable hardware systems
  • FPGA Acceleration
  • AI Hardware

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