Strategic Elements Ltd (ASX:SOR) (OTCMKTS: SORHF) is progressing its proof-of-concept work, with early-stage results show that in the case of computer vision, the technology uses less power to operate than the human brain (less than 10 watts) and can use multiple resistance states with the potential capacity to process multiple points of data.
Recent work has highlighted its printable neuromorphic technology’s potential for data processing and self-learning in soft robotics as well as other signal processing applications such as computer vision applications.
The technology was successfully operated at an ultra-low level of 0.8V and in the microamps range of current.
This printable neuromorphic hardware is being developed from the Company’s Nanocube Memory Ink technology and conducted in the Nanoionics laboratory at the University of New South Wales (UNSW).
Early-stage development of neural network
Artificial neural networks are not uncommon, however most synapse networks exist only as software.
The UNSW team is in early-stage development of a neural network hardware designed to be printable (low cost), portable, ultra-low power, flexible and semi-transparent.
These features are ideally suited to robotics and computer vision applications.
For example, the ability to place flexible neuromorphic hardware onto soft robotics in health or manufacturing sectors or devices requiring such low power that battery or energy harvesting technologies (such as humidity) could potentially be used as a power source.
The new artificial synapse fabricated by the team at UNSW has shown significant advancement in lowering power consumption, ability to continuously change conductance with voltage pulses (like a biological synapse), encouraging endurance and multilevel switching.
Features such as these are important for possible applications such as image processing and smart/intelligent sensors for eskin and soft robotics.
Collaborative and further work
The Company will assess a potential program of work between the computer vision and robotics team at subsidiary Stealth Technologies and the materials team at the University of New South Wales to develop a prototype application. Further information will be released as appropriate.
Future work will also be conducted to reduce the temperature required in the manufacturing process, fabricate on flexible substrates and to increase the number of memristors or artificial synapses in their thousands to meet requirements of image recognition and tactile touch sensors in robotics.
