{"id":32508,"date":"2026-07-08T13:06:00","date_gmt":"2026-07-08T11:06:00","guid":{"rendered":"http:\/\/stocks-future.com\/?guid=a0b072ded1572c4811307b9d10c9440e"},"modified":"2026-07-08T13:06:00","modified_gmt":"2026-07-08T11:06:00","slug":"tetramem-and-sk-hynix-showcase-successful-technology-collaboration-advancing-memory-centric-ai-computing-2","status":"publish","type":"post","link":"https:\/\/stocks-future.com\/?p=32508","title":{"rendered":"TetraMem and SK hynix Showcase Successful Technology Collaboration Advancing Memory-Centric AI Computing"},"content":{"rendered":"

\nJoint 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.<\/b><\/p><\/a><\/a>

SAN JOSE, Calif. & ICHEON, South Korea--(BUSINESS WIRE)--#AIInfrastructure<\/a>--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, \u201cA Memristor-based In-Memory Computing SoC with Efficient Depthwise Convolution,\u201d<\/b> in Advanced Intelligent Systems<\/i>. The work has also been selected as the cover feature<\/b> of the journal, recognizing its technical innovation and potential impact on next-generation AI computing.<\/p>

\nThe collaboration brings together SK hynix\u2019s 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.<\/p>

\nAs 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\u2014compute where the AI model weights live.<\/p>

\nThe 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.<\/p>

\nMore 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.<\/p>

\n\u201cWe are honored to celebrate this important milestone together with SK hynix,\u201d said Glenn Ge, CEO and Co-Founder of TetraMem<\/b>. \u201cThis 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.\u201d<\/p>

\nSoo Gil Kim, Vice President of SK hynix<\/b>, said, \u201cWe are pleased to see the successful outcome of this collaboration and the recognition from Advanced Intelligent Systems<\/i>. 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.\u201d<\/p>

\nThe 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.<\/p>

\nLooking 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.<\/p>

\nThe paper, \u201cA Memristor-based In-Memory Computing SoC with Efficient Depthwise Convolution,\u201d<\/b> is now available online in Advanced Intelligent Systems<\/i>.<\/p>

\nAbout TetraMem<\/b><\/p>

\nTetraMem 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.<\/p>

\nAbout SK hynix<\/b><\/p>

\nSK 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.<\/p> Contacts<\/b>

\nMedia Contact:\nGlenn Ge\npr@tetramem.com<\/a><\/p>","protected":false},"excerpt":{"rendered":"

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.SAN JOSE, Calif. & ICHEO…<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-32508","post","type-post","status-publish","format-standard","hentry","category-infos-businesswire"],"_links":{"self":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts\/32508","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=32508"}],"version-history":[{"count":1,"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts\/32508\/revisions"}],"predecessor-version":[{"id":32510,"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts\/32508\/revisions\/32510"}],"wp:attachment":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=32508"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=32508"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=32508"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}