Neuromorphic & AI Hardware with Nanomaterials: Memristors, RRAM & Synaptic Devices
Building Brain-Inspired Computing Systems with Next-Generation Nanomaterials
About This Course
This three-day workshop (1.5-hour lecture per day) introduces participants to the principles and practical foundations of neuromorphic computing and AI hardware enabled by advanced nanomaterials. The program explores emerging memory and synaptic devices such as Memristor, Resistive Random Access Memory, and nanoscale artificial synapses used for brain-inspired architectures. Participants will learn device physics fundamentals, materials engineering approaches, fabrication considerations, and AI-hardware co-design concepts. The workshop integrates theoretical insights with computational modeling and system-level perspectives to prepare attendees for research and innovation in next-generation intelligent hardware systems.
Aim
To provide foundational and applied knowledge in neuromorphic computing hardware using nanomaterials, focusing on memristive and resistive switching devices for energy-efficient AI systems.
Workshop Objectives
- 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.
Workshop Structure
Day 1: Memristors & Neuromorphic Foundations with Nanomaterials
- Limits of von Neumann architecture and memory bottlenecks
- Introduction to neuromorphic computing principles
- Chua’s memristor theory and I–V hysteresis characteristics
- Resistive switching mechanisms: filamentary vs interface-type
- Nanomaterials in memristive devices:
Metal oxides (TiO₂, HfO₂), Perovskites, 2D materials (MoS₂, graphene), CNTs & hybrid nanocomposites - RRAM basics: SET/RESET physics, endurance, retention
- Crossbar arrays for in-memory analog computation
- Tools: Python, NumPy, Matplotlib, SciPy, Jupyter/Colab
Day 2: Synaptic Devices & Hardware-Aware AI Modeling
- Biological vs artificial synapses
- STDP (Spike-Timing Dependent Plasticity)
- Long-Term Potentiation (LTP) & Depression (LTD)
- Mapping neural weights to device conductance states
- 1R vs 1T1R structures
- Sneak path problem in crossbars
- Analog MAC (Multiply-Accumulate) operations in hardware
- Tools: Python, NumPy, PyTorch (intro), Nengo (neuromorphic simulator), Matplotlib
Day 3: Advanced Neuromorphic Systems & Research-Grade Reporting
- Deep learning on memristive crossbars
- Device non-idealities: non-linearity, drift, stochastic switching
- Quantization and precision trade-offs
- CMOS integration challenges
- Edge AI & event-driven architectures
- Overview of commercial neuromorphic chips
- Tools: TensorFlow/PyTorch (intro), Nengo, NumPy, Scikit-learn (for benchmarking), Jupyter/Colab
Who Should Enrol?
- 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.
Important Dates
Registration Ends
02/17/2026
IST 07:00 PM
Workshop Dates
02/17/2026 – 02/19/2026
IST 08:00 PM
Workshop Outcomes
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.
Fee Structure
Student Fee
₹1699 | $65
Ph.D. Scholar / Researcher Fee
₹2699 | $75
Academician / Faculty Fee
₹3699 | $85
Industry Professional Fee
₹4699 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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