IIT Madras, ISRO Successfully Developed and Booted Indigenous Microprocessor for Space Applications - Related to iit, a, potential, sonar, performance
IIT Madras, ISRO Successfully Developed and Booted Indigenous Microprocessor for Space Applications
IIT Madras and ISRO have developed and booted an indigenous aerospace-grade semiconductor chip based on RISC-V, an open-source Instruction Set Architecture.
The ‘IRIS’ (Indigenous RISCV Controller for Space Applications) chip was developed from the ‘SHAKTI’ processor baseline. This highlights India’s efforts toward self-reliance in semiconductor technology for space applications.
Professor V. Kamakoti, director at IIT Madras, led the SHAKTI microprocessor project at the Prathap Subrahmanyam Centre for Digital Intelligence and Secure Hardware Architecture (PSCDISHA).
The project is backed by the Ministry of Electronics and Information Technology (MeitY) under the ‘Digital India RISC-V’ (DIRV) initiative.
IIT Madras called this a “breakthrough in India’s self-reliance in space tech” in its post on X. The post further broke down the manufacturing process and claimed that the IRIS chip “will power ISRO’s space missions, ensuring advanced fault tolerance & computing reliability.”.
V. Narayanan, chairman of ISRO, also hinted that a flight test may be coming soon! Furthermore, Kamakoti also took to X to display a demo of the chip.
@iitmadras & @isro successfully developed and booted an aerospace-grade, SHAKTI-based semiconductor chip (@ShaktiProcessor) —a breakthrough in India’s self-reliance in space tech!
🔹 Developed with ISRO… [website] — IIT Madras (@iitmadras) February 11, 2025.
This Made-in-India processor was manufactured at SCL (Semiconductor Laboratory) Chandigarh and packaged at Tata Advanced Systems in Karnataka. The motherboard was further developed by PCB Power in Gujarat and assembled by Syrma SGS in Chennai.
“After RIMO in 2018 and MOUSHIK in 2020, this is the third SHAKTI chip we have fabricated at SCL Chandigarh and successfully booted at IIT Madras,” Kamakoti mentioned.
The fact that chip design, fabrication, packaging, motherboard development, assembly, software, and booting all happened within India validates the strength of the country’s semiconductor ecosystem.
The IRIS chip is designed for a range of applications, including IoT and strategic computing needs. This effort aligns with ISRO’s goal of indigenising semiconductors for command and control systems and other critical functions in space missions.
The ISRO Inertial Systems Unit (IISU) in Thiruvananthapuram initiated the development of a 64-bit RISC-V-based controller and collaborated with IIT Madras on specifications and design. The final configuration was tailored to meet the computing requirements of ISRO’s existing sensors and systems.
The design also supports future expansion through multiple boot modes and hybrid memory/device extensions. Extensive software and hardware testing ensured high reliability and performance.
Kamaljeet Singh, director general at SCL Chandigarh, highlighted the role of SCL, saying, “Fabricated in our 180 nm technology node, the processor underwent extensive design validation and testing. SCL remains committed to supporting academia and startups.”.
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TVS Motor to Set Up GCC in Karnataka with a potential investment of ₹2,000 Crore
TVS Motor corporation on Tuesday unveiled plans to invest ₹2,000 crore in Karnataka over the next five years to establish a Global Capability Centre (GCC) and expand its production and engineering facilities in Mysuru.
This initiative aims to boost innovation, attract top talent, and strengthen research capabilities, supporting the firm’s 2030 growth targets.
The investment was revealed at the Global Investors Meet (GIM), Invest Karnataka 2025, where TVS Motor managing director Sudarshan Venu outlined the corporation’s vision.
Venu stated that they envision a capability centre that will attract top talent and innovative ideas and possess research capabilities to serve as the birthplace of next-generation bikes.
The corporation has signed an agreement with the Karnataka government, which includes plans to set up a test track and build new office infrastructure. The centre will bring together engineers, designers, and AI/ML experts to drive future innovations.
Venu stated that the proposed plan seeks to provide substantial solutions for both personal and commercial mobility, establishing new standards.
, TVS Motor already operates a manufacturing facility in Mysuru with over 3,500 employees and an annual production capacity of [website] million vehicles. The factory caters to both domestic and export markets, generating ₹7,600 crore in revenue, with ₹1,200 crore from exports alone.
With the new investment, TVS aims to double its exports and overall revenue from Mysuru operations.
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Perplexity Launches Sonar for Pro Users; Performance on Par with GPT-4o, Claude 3.5 Sonnet
Perplexity, an AI search engine startup, revealed that its in-house model, Sonar, will be available to all Pro clients on the platform. Now, clients with the Perplexity Pro plan can make Sonar the default model via settings.
Sonar is built on top of Meta’s open-source Llama [website] 70B. It is powered by Cerebras Inference, which indicates to be the world’s fastest AI inference engine. The model is capable of producing 1200 tokens per second.
“We optimised Sonar across two critical dimensions that strongly correlate with user satisfaction – answer factuality and readability,” Perplexity showcased, indicating that Sonar significantly improves the base Llama model on these aspects.
Perplexity revealed that their evaluations found that Sonar outperforms OpenAI’s GPT-4o mini and Anthropic’s Claude [website] Haiku and offers performance parity with the bigger models GPT-4o and Claude [website] Sonnet.
Furthermore, Perplexity unveiled Sonar is 10 times faster than Google’s Gemini [website] Flash.
not long ago, French AI startup Mistral revealed its app, Le Chat, which claimed to be the fastest AI assistant in the competition. During our testing, we found it to be faster than all other models. Gemini [website] Flash, on the other hand, came in second. Like Perplexity’s Sonar, Mistral’s Le Chat is also powered by Cerebras Inference.
lately, Perplexity also showcased the availability of the powerful DeepSeek-R1 model on the platform, hosted on servers in the United States.
A few weeks ago, Perplexity introduced that the Sonar API is available in two variants: the Sonar and the Sonar Pro. The business also called it the most affordable API in the market.
The enterprise noted Sonar Pro is “ideal for multi-step tasks requiring deep understanding and context retention”. Moreover, it provides “in-depth answers” with twice the citations of Sonar. The Pro version costs $3 per million input tokens, $15 per million output tokens, and $5 per 1,000 searches, with multiple searches allowed.
The Sonar plan is simpler. It charges $1 per million tokens for input and output and $5 per 1,000 searches, with only one search per request.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Machine Learning | 29% | 38.4% |
| Computer Vision | 18% | 35.7% |
| Natural Language Processing | 24% | 41.5% |
| Robotics | 15% | 22.3% |
| Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Google AI | 18.3% |
| Microsoft AI | 15.7% |
| IBM Watson | 11.2% |
| Amazon AI | 9.8% |
| OpenAI | 8.4% |
Future Outlook and Predictions
The Madras Isro Successfully landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- General AI capabilities
- AI-driven scientific breakthroughs
Expert Perspectives
Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:
"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."
— AI Researcher
"Organizations that develop effective AI governance frameworks will gain competitive advantage."
— Industry Analyst
"The AI talent gap remains a critical barrier to implementation for most enterprises."
— Chief AI Officer
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:
- Improved generative models
- specialized AI applications
- enhanced AI ethics frameworks
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- AI-human collaboration systems
- multimodal AI platforms
- democratized AI development
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- General AI capabilities
- AI-driven scientific breakthroughs
- new computing paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of ai tech evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Responsible AI driving innovation while minimizing societal disruption
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Incremental adoption with mixed societal impacts and ongoing ethical challenges
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and ethical barriers creating significant implementation challenges
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
Transformational Impact
Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.
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platform intermediate
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