MIPS, Cyient Partner to Power AI with Custom RISC-V Chips
The strategic alliance combines RISC-V and analog design to create intelligent, power-efficient chips for high-growth sectors.
June 13, 2025

In a significant move to address the escalating demand for high-performance, domain-specific semiconductors, processor IP leader MIPS and Hyderabad-based Cyient Semiconductor have announced a strategic partnership.[1][2] The collaboration will focus on developing custom silicon solutions, specifically application-specific integrated circuits (ASICs) and application-specific standard products (ASSPs), aimed at the burgeoning automotive, industrial, and data center markets.[3][1] This alliance marries MIPS's deep expertise in RISC-V processor architecture with Cyient's extensive capabilities in analog mixed-signal design and power management, promising to deliver intelligent and efficient chips for a world increasingly reliant on software and AI.[4][5] The partnership arrives at a critical juncture as industries undergo massive transformation, driven by trends like software-defined vehicles, industrial automation, and the relentless expansion of data center infrastructure.[3]
At the core of this collaboration is a fusion of complementary strengths.[5] MIPS, with a 40-year history in RISC computing, brings its Atlas portfolio of processor IP to the table.[4][3] This includes the M8500 microcontroller, which is optimized for the real-time compute workloads characteristic of power efficiency and motor control applications.[5][1] The company's technology is built upon the open RISC-V instruction set architecture (ISA), which offers a crucial advantage by allowing customers to move away from the constraints of proprietary legacy systems.[5][1] This open foundation promotes flexibility and innovation. On the other side of the partnership, Cyient Semiconductor, a Cyient Group company, contributes its significant experience in creating silicon solutions across analog, mixed-signal, RF, and digital domains.[4][1] With over 25 years in the field, Cyient offers end-to-end chip development services, from initial architecture design straight through to production.[5][6] The company’s focus on intelligent power delivery and analog mixed-signal capabilities is seen as a perfect match for MIPS's processor technology.[3][5]
The strategic focus of the MIPS-Cyient alliance is squarely on high-growth sectors where the need for custom, powerful, and efficient chips is most acute. The automotive industry, for instance, is rapidly shifting towards software-defined vehicles (SDVs).[3][7] These next-generation cars rely heavily on a complex interplay of sensors, processors, and software to enable features like advanced driver-assistance systems (ADAS), in-car infotainment, and eventually, full autonomy.[8][9] This evolution demands chips that can handle real-time, safety-critical functions with utmost reliability.[1][10] The partnership aims to deliver solutions for motor control, power delivery management, and other safety-critical applications essential for modern vehicles.[4][10] Similarly, the industrial automation sector is experiencing a surge in the adoption of robotics and AI-powered systems, which require specialized processors for tasks like motor control in industrial robots and intelligent power management in automated factories.[4][10] The data center market is another key target, where the exponential growth of cloud computing and AI workloads necessitates more efficient power delivery and management solutions to handle performance demands while controlling energy consumption.[3][5]
The implications of this partnership for the broader semiconductor and AI industries are substantial. The collaboration champions the use of the RISC-V architecture, an open-source alternative to proprietary ISAs like ARM and x86.[5][11] The open nature of RISC-V democratizes chip design, allowing for greater customization and innovation by enabling developers to add specific instructions tailored for particular workloads, such as those found in AI and machine learning.[12][13] This flexibility is crucial for developing domain-optimized ASICs that can significantly outperform general-purpose processors in both performance and power efficiency for specific tasks.[11][14] By creating custom silicon platforms built on this scalable, open foundation, MIPS and Cyient are enabling their customers to develop advanced, differentiated solutions with a faster time-to-market, while avoiding the proprietary lock-in associated with other architectures.[4][1] This move could accelerate the adoption of RISC-V in high-performance computing, particularly for edge AI and real-time applications where the combination of computational power and energy efficiency is paramount.[11][15]
In conclusion, the strategic alliance between MIPS and Cyient Semiconductor represents a forward-looking response to the evolving demands of the technology landscape. By combining MIPS's RISC-V processor leadership with Cyient's prowess in custom silicon and power management, the partnership is poised to deliver powerful, purpose-built chips for the automotive, industrial, and data center sectors.[4][1] This collaboration not only addresses the immediate need for more intelligent and efficient hardware but also champions a more open and customizable future for semiconductor design. As AI continues to become more deeply integrated into our physical world through autonomous systems and smart infrastructure, the domain-optimized solutions developed through this partnership will likely play a critical role in enabling the next wave of technological innovation, providing the essential building blocks for a more connected and power-efficient world.[3][1]
Research Queries Used
MIPS Cyient Semiconductor partnership details
MIPS RISC-V solutions for automotive and data centers
Cyient semiconductor design services and expertise
Impact of MIPS Cyient collaboration on the semiconductor industry
RISC-V architecture adoption in AI and machine learning
Software-defined vehicles and industrial automation chip demand
MIPS and Cyient joint solutions for high-performance computing
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