惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

爱范儿
爱范儿
WordPress大学
WordPress大学
C
Check Point Blog
GbyAI
GbyAI
U
Unit 42
Google DeepMind News
Google DeepMind News
B
Blog RSS Feed
Blog — PlanetScale
Blog — PlanetScale
J
Java Code Geeks
I
InfoQ
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Hugging Face - Blog
Hugging Face - Blog
Vercel News
Vercel News
博客园 - 【当耐特】
美团技术团队
小众软件
小众软件
S
SegmentFault 最新的问题
Jina AI
Jina AI
阮一峰的网络日志
阮一峰的网络日志
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
The Cloudflare Blog
Last Week in AI
Last Week in AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog

SiliconANGLE

Will agentic AI governance run amok? The lesson of Asimov’s Three Laws - SiliconANGLE AI + quantum, Amazon vs. Starlink and the wide-open US-China internet battle - SiliconANGLE Team Cymru launches Total Insights Feed to replace legacy threat intelligence lists - SiliconANGLE AI Mode in Chrome adds split-screen view to enhance the web search experience - SiliconANGLE Resolve AI raises $40M at $1.5B valuation to optimize production environments - SiliconANGLE How Zscaler and OpenAI turn zero-trust security into an AI accelerator - SiliconANGLE OpenAI ratchets up Codex's agentic capabilities to rival Claude Code - SiliconANGLE Anthropic launches Claude Opus 4.7 with coding, visual reasoning improvements - SiliconANGLE Slash raises $100M at a $1.4B valuation to expand AI-powered banking platform for online businesses - SiliconANGLE Canva unveils Canva AI 2.0, recasting its platform as an agentic system for work - SiliconANGLE Data center, consumer device chips boost TSMC’s revenue - SiliconANGLE Mission-critical security cannot be bolted on, says Oracle - SiliconANGLE Agentic infrastructure reshapes enterprise AI - SiliconANGLE Data quality, and data freedom, foundational for AI success - SiliconANGLE Data trust is a bedrock in successful, scalable AI outcomes - SiliconANGLE Google introduces new agentic AI-ready tools and resources for Android developers  - SiliconANGLE Agentic AI orchestration separates winners from laggards - SiliconANGLE Data-driven tools turning the tide against human trafficking - SiliconANGLE Achieving trusted AI development goes beyond 'vibes' - SiliconANGLE Impinj boosts edge computing power in updated R700 RAIN RFID reader - SiliconANGLE Certinia powers professional services with AI - SiliconANGLE Antioch prepares to accelerate simulated testing for autonomous robots after raising $8.5M - SiliconANGLE Developer tooling startup Expo nabs $45M investment - SiliconANGLE Solidroad lands $25M to bring AI to customer support interactions - SiliconANGLE DuploCloud lands compliance and AI governance certifications as enterprise buyers tighten scrutiny - SiliconANGLE Lua lands $5.8M to help businesses build and manage AI agent workforces - SiliconANGLE Best of frenemies: Oracle's and AWS' clouds unite with dedicated, private connectivity - SiliconANGLE NIST shifts National Vulnerability Database to risk-based triage as CVE submissions hit record levels - SiliconANGLE Cisco goes to the races with new Churchill Downs multiyear partnership - SiliconANGLE Susecon 2026 will tackle the future of open-source platforms - SiliconANGLE
XCENA raises $135M for its computational memory controlle...
by Maria Deutscher · 2026-05-30 · via SiliconANGLE

UPDATED 19:05 EDT / MAY 29 2026

INFRA

XCENA raises $135M for its computational memory controller

XCENA Inc., a startup with a memory device designed to speed up artificial intelligence clusters, today announced that it has raised $135 million in funding.

The Series B round was led by Korean funds Atinum Investment and IMM Investment. XCENA says that the raise also included contributions from more than a half dozen other institutional backers. The company is now valued at $570 million.

XCENA was founded in 2022 by former employees of Samsung Electronics Co. and SK hynix Inc., the world’s top suppliers of memory for graphics cards. Its flagship product is a device called the MX1 that it describes as a computational memory controller. It’s designed to speed up the data management tasks involved in running AI inference workloads.

Large language models use a data structure called a KV cache to interpret user prompts. When the KV cache can’t fit in a graphics card’s built-in memory, it has to be offloaded to slower external DRAM, which creates processing delays. A similar issue affects the vector databases that many LLMs use to store information.

XCENA says the MX1 addresses the challenge. The device combine up to two terabytes of DRAM with several thousand central processing unit cores. It can hold an LLM’s KV cache and vector databases without the performance issues that affect traditional memory devices. The result is an increase in inference performance.

Another way the device accelerates AI workloads is by reducing the need for duplicate calculations. Many LLMs refresh their KV cache, the data structure they use to interpret prompts, after every user request. MX1 makes it possible to reuse the same KV cache across requests and thereby reduce processing overhead. 

The company says the chip can also accelerate analytics applications such as Apache Spark. Such workloads regularly move data between the CPUs on which they run and the memory they use to hold data. The MX1’s memory pool and CPU cores are closer to each other than the components of a standard server, which reduces data travel times.

The device’s CPU cores are based on the open-source RISC-V architecture. They’re organized into four-core clusters that each have a dedicated L1 cache, a type of high-speed memory. The four-core clusters are organized into larger clusters that likewise have an integrated memory pool.

XCENA provides application programming interfaces that enable developers to port their AI workloads to the MX1 without major code changes. According to the company, customers with more advanced requirements have access to a second set of APIs that can be used to make low-level performance optimizations. It also provides a simulation tool that eases software reliability testing. 

The company plans to make the MX1 using Samsung’s four-nanometer chip manufacturing process. According to TechCrunch, it will begin mass production by the end of the year and expects to start generating revenue in 2027.

The company will use the proceeds from its funding round to develop new computational memory products. In addition, it plans to accelerate its go-to-market efforts and establish partnerships with key industry players such as hyperscalers.

Image: Xcena

A message from John Furrier, co-founder of SiliconANGLE:

Support our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.

  • 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more
  • 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network.

About SiliconANGLE Media

SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE, theCUBE Network, theCUBE Research, CUBE365, theCUBE AI and theCUBE SuperStudios — with flagship locations in Silicon Valley and the New York Stock Exchange — SiliconANGLE Media operates at the intersection of media, technology and AI.

Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.