People of India travel distances by personal vehicles for flat to day activities. By Technology the people of certain locus such as a community gated , apartments or colony etc who travel same route and to particular point of destiny may have Technical apps made to travel together in the said personal vehicle. May share cost of fuel etc. like School and institutional buses cabs etc have common pick up and drop points for a destiny same with private-public can see this workable technology. This may increase rapport and also decrease stranger feel among public daily activities.

AI-driven chip design optimization to reduce development time. Predictive maintenance for semiconductor manufacturing equipment. AI-based yield improvement and defect detection. Low-power AI chips for edge computing and IoT devices. Digital twin simulations for semiconductor fabrication plants. AI-powered energy and resource optimization for sustainable manufacturing. Generative AI assistant for engineering knowledge and troubleshooting. Automated test generation and validation using AI. AI-enabled semiconductor supply chain forecasting and risk management. Specialized AI accelerators for real-time simulation, graphics, and analytics.

The intersection of Artificial Intelligence and semiconductors is driving a powerful dynamic: AI demands custom, high-efficiency hardware, while semiconductor design and manufacturing rely on AI to break past physical scaling limits. ​AI-Accelerated Chip Design (EDA) AI models are transforming Electronic Design Automation (EDA) by solving complex optimization problems in layout design and signal routing. Reinforcement learning algorithms can explore billions of floorplan configurations in a fraction of the time traditional software takes, optimizing for Power, Performance, and Area (PPA) while reducing development costs. ​Domain-Specific & Non-Von Neumann Architectures Traditional computing architectures struggle with the data-movement bottlenecks of massive AI workloads. Emerging hardware solutions target these limits directly: ​In-Memory Computing: Computes directly within memory arrays (using ReRAM or MRAM) to drastically cut energy consumption from data transfer.

अगर हम ऐसे इको एटीएम शुरू कर सकें जिनमें पुराने या खराब फोन िमा जकए िा सकें और बदले में ग्राहक नकद या यूपीआई के माध्यम से ऑनलाइन भुगतान प्राप्त कर सकें, तो यह भारत में एक क्ाांजतकारी कदम होगा। अगर हम ग्रामीण बैंकोां और भारतीय स्टेट बैंक की शाखाओां में ऐसे इको एटीएम स्थाजपत कर दें जिनमें पुराने या खराब फोन िमा जकए िा सकें, तो मोबाइल सुरजित रहेंगे और डेटा चोरी से सुरजित रहेगा। अगर ग्राहक को फोन का मूल्य जमल सके, तो हम कैजशफाई और बैंकोां की मदद से इस इको एटीएम पररयोिना को शुरू कर सकते हैं। यह एक शानदार पररयोिना होगी और हम पुराने फोन उनकी कांपजनयोां के अनुसार बेच सकेंगे और अगर कांपनी बांद हो गई है, तब भी हम मोबाइल फोन स्वीकार कर सकते हैं।

Hi

Dear honorable ministers, Advances in sciences translate into technologies and hence the importance of research programs. Expansion of academic programs is crucial for the nation as it builds a vast talent pool in all subjects of study. Here is an example of important areas of research that the nation cannot delay any longer as national security depends upon it

Innovation / Technical Perspective Three innovations that will emerge at this intersection: 1. Neuromorphic Chips: Chips that work like the human brain, not like traditional computers - 1000x more energy efficient for AI. 2. AI-designed Chips: Google already used AI to design its TPU in weeks instead of months. Soon AI will design semiconductors we humans can't even imagine. 3. Sustainable AI Hardware: Today's AI training consumes massive energy. The next wave will be semiconductors made with 2D materials (like Graphene) that make AI sustainable. Innovation must be sustainable, as India's BRICS theme says.

AI and semiconductors are no longer two separate industries. They are becoming one technology stack, AI determines what compute is needed, while semiconductors determine what AI is economically possible. India shouldn't ask, “How do we become another TSMC?” or “How do we build an Indian NVIDIA?” Instead, the question should be: “Where in the semiconductor + AI value chain can India become indispensable?” NITI Aayog's current roadmap makes a similar argument: rather than trying to win every part of the semiconductor race, India should focus on areas where it can leapfrog, particularly design, advanced packaging, compound semiconductors, materials and system architecture. One mistake I see in discussions about semiconductors is focusing too much on: “We need to manufacture chips.” The higher-value question is: “What complete products can we build around those chips?” For example: AI chip + software + board + sensors + model + cloud platform It could become a complete product.

From my perspective, India’s semiconductor journey should go beyond manufacturing and move towards designing the advanced computing systems that will power the next generation of AI. India should focus on indigenous AI-powered GPUs, NPUs, AI optimized CPUs, TPUs and specialized accelerators, along with silicon photonics, high-speed interconnects and advanced networking chips. Dedicated silicon for data movement, memory, storage and security can reduce bottlenecks and improve overall efficiency. Future architectures could combine CPU, GPU, NPU and QPU technologies, enabling heterogeneous computing for AI, scientific research and quantum applications. India should also explore ultra low precision computing at the hardware level to achieve more AI computation with the same energy. The goal should be not only to manufacture chips, but to design complete AI computing platforms from chip architecture and advanced packaging to interconnects, networking and AI software. click pdf for more info

AI governance: Use AI to detect tax evasion, welfare leakage, fraud and duplicate beneficiaries. AI for Indian languages: Build high-quality models for Hindi, Telugu, Tamil, Bengali, Marathi and other Indian languages.