Papers
6
Total Citations
441
H-Index
4
About
Daniel Neil is a researcher at the forefront of neuromorphic computing and event-driven machine vision, with contributions that have significantly shaped how the field thinks about efficient neural network architectures. His most influential work, "Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator" (2014, 256 citations), introduced a scalable hardware architecture for spiking neural networks, addressing critical computational challenges that conventional deep learning frameworks struggle to overcome. This work established him as a key figure in bridging theoretical neuroscience-inspired models with practical, deployable hardware. Neil has also made important strides in event-based computer vision, particularly through his research using the Dynamic and Active-Pixel Vision Sensor (DAVIS). His work on combined frame- and event-based detection and tracking (89 citations) and predator-prey robotic applications demonstrated real-world viability for this emerging sensing paradigm. His 2016 paper "Learning to be Efficient" (82 citations) further explored the inherent computational advantages of spiking neural networks, showing how their asynchronous, sparse activity translates into meaningful energy savings. Together, his body of work reflects a sustained commitment to making intelligent systems faster, leaner, and biologically inspired.
Research Focus
Key Achievements
Top Papers
- 1Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator256 citations · 2014
- 2Combined frame- and event-based detection and tracking89 citations · 2016
- 3Learning to be efficient82 citations · 2016
- 4
- 5
- 6