Neuromorphic & AI Hardware with Nanomaterials: Memristors, RRAM & Synaptic Devices

To provide foundational and applied knowledge in neuromorphic computing hardware using nanomaterials, focusing on memristive and resistive switching devices for energy-efficient AI systems.

• Understand the fundamentals of neuromorphic computing and brain-inspired architectures.
• Explore the physics and switching mechanisms of memristors and RRAM devices.
• Learn the role of nanomaterials in enabling synaptic plasticity and non-volatile memory behavior.
• Analyze structure–property relationships in nanoscale resistive devices.
• Understand fabrication techniques and material selection for synaptic hardware.
• Study AI–hardware co-design principles for edge and low-power computing.
• Examine device characterization parameters such as endurance, retention, switching speed, and variability.
• Explore integration of memristive devices into neural network architectures.
• Discuss challenges in scalability, reliability, and commercialization of neuromorphic hardware.
• Identify emerging research directions in intelligent, adaptive, and energy-efficient computing systems.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

By the end of the workshop, participants will be able to:

• Explain the operating principles of memristive and resistive switching devices.
• Relate nanomaterial properties to device performance metrics.
• Interpret I–V characteristics and switching behavior in synaptic devices.
• Understand hardware implementation of artificial neural networks.
• Evaluate the potential of neuromorphic systems for low-power AI applications.
• Propose conceptual designs for nanoscale AI hardware systems.