In-Memory Computing SoC with Multi-level RRAM to Accelerate AI Inference

July 18, 2025

In-Memory Computing SoC with Multi-level RRAM to Accelerate AI Inference

TetraMem will introduce its multi-level RRAM cell for in-memory computing. The talk will explain how TetraMem uses Multi-level RRAM to accelerate neural network inference applications. The speaker will demonstrate how TetraMem leverages its unique technology and expertise to increase precision, accuracy and energy efficiency of AI applications, including our methods to improve cell performance as recently published in Nature and Science.

SK hynix-TetraMem collaboration featured on Tom’s Hardware

Read how Tom’s Hardware spotlights TetraMem’s joint work with SK

July 16, 2026

TetraMem and SK hynix Announce Breakthrough in Analog In‑Memory Computing for Next‑Generation AI

Our joint research paper with the SK Hynix, “A Memristor-based In-Memory Computing SoC with Efficient Depthwise Convolution,” has been accepted and published online in Advanced Intelligent Systems https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202501225

June 13, 2026

TetraMem Completes MLX200 Silicon Validation: A Breakthrough in Analog In-Memory Computing for AI

TetraMem Completes MLX200 Silicon Validation. TetraMem’s MLX200 is the first multi-level RRAM analogue in-memory computing SoC on 22nm silicon, delivering energy-efficient AI inference for edge, IoT, and wearables.

May 19, 2026

TetraMem Achieves Historic Milestone with High Temp 700°C RRAM for Extreme-Environment AI Computing

TetraMem and researchers from the University of Southern California and the University of Massachusetts Amherst demonstrated RRAM (memristor) devices capable of reliable operation at temperatures up to 700°C. Science article “High-temperature memristors enabled by interfacial engineering.” https://www.science.org/doi/10.1126/science.aeb9934

May 19, 2026