This paper investigates aspects of bio-inspired models that help create more energy efficient methods in pattern recognition. A comparison between a biologically plausible pattern recognition approach and a purely computer based (algorithmic) approach yielded three main findings. Firstly, the occurrence of low-complexity parallel sub-processes within the bio-inspired approach allows higher energy efficiency by relaxing the requirement of having faster processors. Secondly, the bio-inspired approach takes advantage of numerous computationally inexpensive sub-processes that will scale better in massively parallel environments, such as neuromorphic computers, thus providing comparable speed. Finally, it is far more easier to adapt across a range of application domains than its algorithmic counterpart.
- Distributed pattern recognition
- Strong AI