New in-memory computing chip promises faster processing with lower energy use

SK hynix and TetraMem have developed a new analog in-memory computing chip that processes AI workloads directly inside memory, dramatically cutting energy use and latency, and if scalable, could be the answer to one of the biggest challenges the AI industry is facing today.

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A new partnership between SK Hynix and TetraMem has combined SK Hynix's advanced memory expertise with TetraMem's analog computing, creating a prototype device that uses memristor-based in-memory computing to perform efficient depthwise convolution, a key operation carried out by AI inference models.

By processing data where it's stored, the system bypasses the energy-heavy data transfers between memory and compute units. The companies claim this architecture addresses a major bottleneck in modern AI hardware as models scale to trillions of parameters. The device demonstrates that it is possible to process data where AI model weights are stored, and with this new architecture, it dramatically reduces the need to transfer between memory and processors, reducing overall power consumption, latency, and generated heat within the system.

"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," said Glenn Ge, CEO and Co-Founder of TetraMem

Traditional AI chips involve constant data movement, which consumes both time and energy. Analog in-memory computing shifts the workflow by performing matrix calculations directly within the memory array. The joint project combined emerging memory devices, circuit design, AI architecture, and software to create the new chip. The result is a practical AI system-on-chip that demonstrates the viability of memory-centric computing.

"We are honored to celebrate this important milestone together with SK Hynix. This achievement demonstrates what can be accomplished through close collaboration across the semiconductor ecosystem," said Ge

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Question #1

What thermal and cooling implications did the prototype demonstrate under sustained inference workloads in the published study?

The published study reported that the prototype dramatically reduced generated heat within the system by performing computations where weights are stored, cutting energy use and the need for power- and heat-intensive data transfers between memory and processors. It therefore demonstrated improved thermal behavior under sustained inference workloads by lowering overall power consumption and reducing latency and system heat.

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The collaboration marks SK Hynix's move toward advanced computing architectures beyond traditional memory manufacturing. The study, published in Advanced Intelligent Systems, highlights the potential of in-memory computing for future AI systems, and given the success of this collaboration, we will undoubtedly see future iterations of this technology in the years to come.