- Joint achievement highlights how Analog In-Memory Computing can address the growing energy and thermal challenges of AI while laying the foundation for deeper collaboration on next-generation memory and computing architectures.
TetraMem Inc., a leader in Analog In-Memory Computing (A-IMC) technology, and SK hynix Inc., a global leader in AI memory and semiconductor technologies, today announced the successful completion of a joint technology collaboration, highlighted by the publication of their research paper, “A Memristor-based In-Memory Computing SoC with Efficient Depthwise Convolution,” in Advanced Intelligent Systems. The work has also been selected as the cover feature of the journal, recognizing its technical innovation and potential impact on next-generation AI computing.
The collaboration brings together SK hynix’s expertise in advanced memory technologies and TetraMem‘s Analog In-Memory Computing platform to explore new computing architectures capable of addressing one of artificial intelligence’s most pressing challenges: reducing the energy consumption and thermal limitations associated with rapidly growing AI workloads.
As foundation models continue to scale from billions to trillions of parameters, data movement between processors and memory has become a dominant contributor to system power consumption, latency, and thermal challenges. Analog In-Memory Computing (A-IMC) addresses this bottleneck with a fundamentally different architecture by performing matrix operations directly where the model weights reside, dramatically reducing data movement while improving system-level performance and energy efficiency—compute where the AI model weights live.
The published work demonstrates a memristor-based AI System-on-Chip (SoC) implementing efficient depthwise convolution, an important building block for modern AI inference workloads. Beyond demonstrating the feasibility of Analog In-Memory Computing, the project showcases the successful integration of emerging memory devices, circuit design, AI architecture, software, and system optimization into a practical semiconductor platform.
More importantly, the project reflects the strong engineering collaboration between the SK hynix RTC and TetraMem teams, combining complementary expertise to advance memory-centric AI computing technologies.
“We are honored to celebrate this important milestone together with SK hynix,” said Glenn Ge, CEO and Co-Founder of TetraMem. “This achievement demonstrates what can be accomplished through close collaboration across the semiconductor ecosystem. As AI continues to evolve, breakthroughs will require innovation not only in compute, but also in memory and system architecture. We believe memory-centric computing and Analog In-Memory Computing will become increasingly important technologies for addressing future AI energy efficiency and thermal challenges, and we look forward to continuing our collaboration with SK hynix.”
Soo Gil Kim, Vice President of SK Hynix, said, “We are pleased to see the successful outcome of this collaboration and the recognition from Advanced Intelligent Systems. This project demonstrates the value of exploring innovative memory technologies and new computing architectures for future AI systems. We appreciate the excellent collaboration with the TetraMem team and look forward to continued technical exchanges in areas of mutual interest.”
The selection of the work as the journal's cover feature further recognizes the significance of the joint achievement and the growing importance of memory-centric computing within the AI industry.
Looking ahead, both companies recognize that future AI infrastructure will require continued advances across memory technology, computing architecture, and system integration to address increasing demands for performance, energy efficiency, and sustainable computing. Building upon the success of this collaboration, the two organizations look forward to exploring additional opportunities for technical collaboration that advance next-generation AI computing technologies.
The paper, “A Memristor-based In-Memory Computing SoC with Efficient Depthwise Convolution,” is now available online in Advanced Intelligent Systems.
About TetraMem
TetraMem Inc. is a Silicon Valley semiconductor company pioneering Analog In-Memory Computing (A-IMC) based on multi-level memristor (RRAM) technology. Its memory-centric AI computing platform enables high-performance, energy-efficient AI inference for edge, enterprise, and future data center applications.
About SK Hynix
SK hynix Inc. is a global semiconductor company and a leading supplier of HBM, NAND Flash, and advanced AI memory solutions. The company continues to develop innovative memory technologies that power next-generation AI, high-performance computing, and data-centric applications worldwide.
View source version on businesswire.com: https://www.businesswire.com/news/home/20260708244132/en/
언론연락처: TetraMem Inc. Glenn Ge
이 뉴스는 기업·기관·단체가 뉴스와이어를 통해 배포한 보도자료입니다.
The collaboration brings together SK hynix’s expertise in advanced memory technologies and TetraMem‘s Analog In-Memory Computing platform to explore new computing architectures capable of addressing one of artificial intelligence’s most pressing challenges: reducing the energy consumption and thermal limitations associated with rapidly growing AI workloads.
As foundation models continue to scale from billions to trillions of parameters, data movement between processors and memory has become a dominant contributor to system power consumption, latency, and thermal challenges. Analog In-Memory Computing (A-IMC) addresses this bottleneck with a fundamentally different architecture by performing matrix operations directly where the model weights reside, dramatically reducing data movement while improving system-level performance and energy efficiency—compute where the AI model weights live.
The published work demonstrates a memristor-based AI System-on-Chip (SoC) implementing efficient depthwise convolution, an important building block for modern AI inference workloads. Beyond demonstrating the feasibility of Analog In-Memory Computing, the project showcases the successful integration of emerging memory devices, circuit design, AI architecture, software, and system optimization into a practical semiconductor platform.
More importantly, the project reflects the strong engineering collaboration between the SK hynix RTC and TetraMem teams, combining complementary expertise to advance memory-centric AI computing technologies.
“We are honored to celebrate this important milestone together with SK hynix,” said Glenn Ge, CEO and Co-Founder of TetraMem. “This achievement demonstrates what can be accomplished through close collaboration across the semiconductor ecosystem. As AI continues to evolve, breakthroughs will require innovation not only in compute, but also in memory and system architecture. We believe memory-centric computing and Analog In-Memory Computing will become increasingly important technologies for addressing future AI energy efficiency and thermal challenges, and we look forward to continuing our collaboration with SK hynix.”
Soo Gil Kim, Vice President of SK Hynix, said, “We are pleased to see the successful outcome of this collaboration and the recognition from Advanced Intelligent Systems. This project demonstrates the value of exploring innovative memory technologies and new computing architectures for future AI systems. We appreciate the excellent collaboration with the TetraMem team and look forward to continued technical exchanges in areas of mutual interest.”
The selection of the work as the journal's cover feature further recognizes the significance of the joint achievement and the growing importance of memory-centric computing within the AI industry.
Looking ahead, both companies recognize that future AI infrastructure will require continued advances across memory technology, computing architecture, and system integration to address increasing demands for performance, energy efficiency, and sustainable computing. Building upon the success of this collaboration, the two organizations look forward to exploring additional opportunities for technical collaboration that advance next-generation AI computing technologies.
The paper, “A Memristor-based In-Memory Computing SoC with Efficient Depthwise Convolution,” is now available online in Advanced Intelligent Systems.
About TetraMem
TetraMem Inc. is a Silicon Valley semiconductor company pioneering Analog In-Memory Computing (A-IMC) based on multi-level memristor (RRAM) technology. Its memory-centric AI computing platform enables high-performance, energy-efficient AI inference for edge, enterprise, and future data center applications.
About SK Hynix
SK hynix Inc. is a global semiconductor company and a leading supplier of HBM, NAND Flash, and advanced AI memory solutions. The company continues to develop innovative memory technologies that power next-generation AI, high-performance computing, and data-centric applications worldwide.
View source version on businesswire.com: https://www.businesswire.com/news/home/20260708244132/en/
언론연락처: TetraMem Inc. Glenn Ge
이 뉴스는 기업·기관·단체가 뉴스와이어를 통해 배포한 보도자료입니다.
ⓒ 주식회사 에이아이크리에이티브랩 & www.aifilmjournal.com 무단전재-재배포금지
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