Master’s in AI Chip Architecture & Agentic EDA Orchestration trains engineers to design intelligent AI processors using autonomous EDA workflows and next-gen silicon innovation.
Level
Advanced
Duration
16 weeks



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Master’s in AI Chip Architecture & Agentic EDA Orchestration is an advanced, industry-focused program designed for engineers who want to lead next-generation semiconductor innovation. This program trains you to design high-performance AI processors, optimize silicon architectures, and implement intelligent, agent-driven EDA workflows. You will master AI accelerator design, chip architecture modeling, power and performance optimization, and autonomous design orchestration. With real-time projects and hands-on exposure to modern EDA tools, you gain the expertise required for AI hardware engineering, advanced SoC design, and intelligent semiconductor automation.
Working professional who is carrying more then 10 years of industry experience.
Access to updated presentation decks shared during live training sessions.
E-book provided by TechPratham. All rights reserved.
Module-wise assignments and MCQs provided for practice.
Daily Session would be recorded and shared to the candidate.
Live projects will be provided for hands-on practice.
Expert-guided resume building with industry-focused content support.
Comprehensive interview preparation with real-time scenario practice.
Foundations of Semiconductor & Digital Design
Foundations of Semiconductor & Digital Design builds core knowledge in CMOS technology, logic gates, digital circuits, timing concepts, and VLSI basics essential for advanced chip architecture.
Computer Architecture & AI Fundamentals
Computer Architecture & AI Fundamentals covers processor design, memory hierarchy, parallel computing, and core AI concepts to build efficient, high-performance intelligent systems.
AI Accelerator & NPU Architecture
AI Accelerator & NPU Architecture focuses on designing high-performance neural processing units, optimizing MAC arrays, memory bandwidth, and parallel dataflow for efficient AI computation.
Verification & Agentic RTL Development
Verification & Agentic RTL Development focuses on building robust testbenches, functional coverage, and AI-driven RTL automation to ensure reliable, high-quality chip design and faster verification closure.
Advanced AI Chip Design & Agentic EDA Tools Stack
This module equips you with industry-relevant tools required for AI chip architecture, intelligent RTL development, and autonomous EDA orchestration. You gain hands-on expertise in programming, hardware description, machine learning, and physical design automation to build next-generation semiconductor solutions.
Physical Design & Timing Fundamentals
Physical Design & Timing Fundamentals covers placement, routing, clock tree synthesis, static timing analysis, and timing closure techniques to ensure optimal performance, power efficiency, and reliable silicon implementation.
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AI Chip Architect
AI Accelerator Design Engineer
RTL Design Engineer
Physical Design Engineer
EDA Automation Engineer
AI Hardware Engineer
SoC Design Engineer
Timing Analysis Engineer
Post-Silicon Validation Engineer
Semiconductor Machine Learning Engineer
AI Chip Architect
AI Accelerator Design Engineer
RTL Design Engineer
In this project, learners architect a complete AI accelerator from specification to verification. You will define compute units, pipelines, memory hierarchy, and dataflow; implement RTL using HDLs; and verify functionality using simulation and testbenches. Master modern AI chip design workflows used in industry. • Design MAC units, tensor cores, and an optimized memory hierarchy. • Develop RTL using Verilog/SystemVerilog with modular architecture. • Build testbenches and perform functional and coverage verification. • Analyze throughput, latency, and bandwidth for performance tuning.Key Highlights:
Build an intelligent EDA orchestration system that uses agentic AI to automate floorplanning and optimization. Integrate AI agents with standard EDA tools to explore design trade-offs, minimize area, reduce wirelength, and enforce timing constraints. Learn how AI transforms chip design productivity. • Develop AI agents for automated placement and routing optimization. • Implement timing closure, congestion reduction, and area balancing. • Integrate scripts with industry EDA tools for adaptive workflows. • Compare AI-driven and traditional floorplanning efficiency metrics.Key Highlights:
This project focuses on synthesizing an AI chip with performance, power, and area optimization. You will use high-level synthesis (HLS), power analysis tools, and agent-assisted optimization loops to balance throughput and efficiency, mirroring workflows used in cutting-edge semiconductor design. • Execute the RTL-to-GDS flow, including synthesis and timing analysis. • Perform static and dynamic power optimization techniques. • Apply AI-assisted optimization to improve PPA (Power, Performance, and Area). • Validate the design across multiple operating corners and constraints.Key Highlights:

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