Neuromorphic computers represents a paradigm shift in artificial intelligence, directly inspired by the operational mechanisms of the human brain. Designed to reproduce high brain efficiency, neuromorphic systems use protocols based on events focused on events, considerably reducing energy consumption while allowing the processing of data in real time. At the heart of these systems are neural networks (SNN), alongside specialized equipment such as programmable door networks (FPGA), Memristors and integrated circuits specific to application (ASIC). This approach is particularly valuable for EDGE computer devices, such as smartphones, drones and IoT systems – where the demand for rapid adaptive responses, low energy consumption and effective learning processes are essential. The transformer potential for neuromorphic computer science extends in many sectors, including robotics and environmental surveillance, suggesting a future where AI is both more sustainable and adaptable in an omnipresent way.
The objective of this research subject is to resolve and overcome the gaps in traditional calculation architectures that vacillate in the supply of energy and adaptive efficient solutions capable of real -time processing, in particular in EDGE computer environments. The growing requirements for intelligent automation in areas ranging from IoT to autonomous robotics require a difference compared to the heavy -length conventional calculation methods of resources. Neuromorphic computer science emerges as a formidable competitor in this space, taking advantage of its bio-inspired conceptions to facilitate power preservation operations and focused on events via SNN and advanced material solutions.
In the pursuit of deeper perspectives and technological progress, this subject focuses both on the progression of neuromorphic material and the evolution of SNN algorithms, which are crucial for end -up learning in practical scenarios. Interdisciplinary collaboration is encouraged to discover viable strategies for the implementation of neuromorphic systems in various fields, thus preparing the way for revolutionary IA innovations which offer low -power and very adaptable computer alternatives. The specific areas of interest to be submitted include:
o Neuromorphic and cognitive computer equipment: investigate new materials, devices and structural conceptions that optimize neuromorphic treatment.
O SNN and cognitive algorithms: development of new models and training techniques that improve the capacities of adaptive learning in real time.
o Applications in Edge, IoT, Robotics and Cognitive Calculation: Case studies that examine the deployment and impact of neuromorphic computers in mobile, robotic and sensors platforms.
o Integrative interdisciplinary research: Studies which merger neuromorphic computer science with other areas such as robotics, environmental surveillance or cognitive sciences.
We invite submission to original research, complete journals and insightful case studies which postpone the limits of neuromorphic and cognitive computers, in particular in environments limited by resources and require robust solutions oriented towards edges.
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- General comment
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Keywords: Neuromorphic computer, peak neural networks, cognitive IT, edges processing, cognitive edge peripherals
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