IEPL Webex Interactive Globalversity

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Neuromorphic and Brain-Inspired Chips
VLSI is key to developing neuromorphic hardware, which mimics the brain’s architecture.
Examples include Intel’s Loihi and IBM’s TrueNorth—designed for ultra-low-power AI processing, especially in edge environments.
Also
3D ICs and chiplets will allow vertical stacking and modular integration—critical for parallel AI workloads.

VLSI (Very Large Scale Integration) and AI (Artificial Intelligence) are deeply complementary domains, and their convergence is driving the next generation of intelligent hardware and systems. Here's an overview of how they come together:

1. AI in VLSI Design
AI/ML is used to automate, optimize, and accelerate various stages of chip design:
● EDA Tools Powered by AI
AI helps Electronic Design Automation (EDA) tools in logic synthesis, placement, routing, and verification.
● Predictive Modeling
AI models predict timing, power consumption, and thermal performance early in the design cycle.
● Design Space Exploration
Reinforcement learning helps explore millions of possible architectures and configurations to find optimal layouts and resource allocations.
2. VLSI for AI Acceleration
As AI workloads grow, VLSI provides dedicated hardware to run these models faster and more efficiently:
● AI Accelerators
ASICs (Application-Specific Integrated Circuits): e.g., Google TPU (Tensor Processing Unit
● Neuromorphic Chips
Mimic the structure of the human brain.

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Welcome to IEPL GlobalVersity With 16 years of expertise, market awareness and management acumen, IEPL Globalversity is a Co-Company of automation brand IEPL. By virtue of tie ups with world class universities and focus on hands on training, we hope to give holistic, simple and efficient delivery of courses offered by us.