Artificial Intelligence (AI) and Machine Learning (ML) applications are driving increased demand for high-performance, low-power memory solutions across consumer, medical, and industrial markets. These applications require efficient, Non-Volatile Memory (NVM) to process, store, and retain large volumes of data, as well as support frequent Firmware Over-The-Air (FOTA) updates.
In the consumer space, remote FOTA updates mitigate security vulnerabilities, introduce new features, and optimize performance for connected devices like smartphones, wearables, and smart appliances. Many of these devices also store data and error logs locally before uploading to the cloud. Similarly, stand-alone and interconnected medical devices and telehealth solutions receive routine FOTA updates to implement the latest security protocols, introduce new functionality, and ensure Health Insurance Portability and Accountability Act (HIPAA) compliance for archived patient data and analytics. In factories, automated embedded Industrial IoT (IIoT) systems and Industry 4.0 manufacturing processes rely on data logging and resilient firmware management for predictive maintenance, customer KPIs, and optimal operation.
Traditional Non-Volatile Memories (NVMs) such as NOR Flash (Non-Volatile Read Only Memory) and EEPROM (Electrically Erasable Programmable Read-Only Memory) can’t efficiently support the evolving requirements of AI-driven applications due to slow data access times, limited endurance and capacity, and high-power consumption. In contrast, high-performance, low-power Resistive Random Access Memory (RRAM) offers rapid, non-volatile read-write capabilities, high endurance, and scalable storage capacity for both FOTA updates and data management.
This white paper reviews key firmware and data management challenges, discusses the limitations of traditional NVM technologies in this context, and explores the advantages of Resistive RAM (RRAM) compared to other NVM solutions. For more detailed information, download our exclusive whitepaper!
