TY - JOUR
T1 - Improving energy consumption of pattern recognition by combining processor-centric and bio-inspired considerations
AU - Hettiarachchige, Yathindu R.
AU - Khan, Asad I.
AU - Barca, Jan Carlo
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Bio-inspired
KW - Distributed pattern recognition
KW - Parallelism
KW - Strong AI
UR - http://www.scopus.com/inward/record.url?scp=85041354507&partnerID=8YFLogxK
U2 - 10.1016/j.bica.2018.01.004
DO - 10.1016/j.bica.2018.01.004
M3 - Article
AN - SCOPUS:85041354507
SN - 2212-683X
VL - 23
SP - 54
EP - 63
JO - Biologically Inspired Cognitive Architectures
JF - Biologically Inspired Cognitive Architectures
ER -