The impact of artificial intelligence is everywhere: from the data center to the edge

Generative artificial intelligence has become an important driving force for the transformation of artificial intelligence and has had a broad and far-reaching impact on our daily lives. In the past year, artificial intelligence technology has gradually penetrated into consumers’ lives. News and product announcements from MWC 2024 highlight the potential of next-generation generative AI applications. This technology will be everywhere, integrated directly into edge and endpoint devices, driving creativity and communication to unprecedented heights.

"Edge artificial intelligence" refers to the deployment of artificial intelligence algorithms into network edge infrastructure and directly to terminals, such as smartphones, cameras, sensors and IoT devices, thereby eliminating the need for cloud servers. Achieve real-time processing and decision-making under the circumstances. This decentralization of AI processing offers several advantages, including reduced latency, enhanced privacy, and improved reliability where internet connectivity is limited.

For example, suppose your smart watch has artificial intelligence edge capabilities. This means the watch can perform some tasks locally without having to rely on cloud services. For example, watches can leverage built-in artificial intelligence models to perform tasks such as speech recognition, translation, and health monitoring to provide fast response times. This approach to edge computing not only speeds up processing, it also helps protect personal privacy because it reduces the need for external servers to transfer data.

The key to edge artificial intelligence lies in efficient reasoning capabilities, that is, the use of trained artificial intelligence models to make predictions or decisions. To improve performance, specialized memory technology needs to be employed and customized to the specific needs of the end device. As larger models provide greater accuracy and result fidelity, the need for greater memory capacity and bandwidth will continue to grow within device power and space constraints.

Designers have many options when choosing memory for AI/ML inference, but when it comes to the key parameter bandwidth, GDDR memory performs well. For mobile phones and many IoT devices, power consumption and space constraints are critical, making LPDDR the memory of choice. When selecting memory for edge AI inference, you need to strike the right balance between bandwidth, capacity, power consumption, and compact form factor.

Securing edge and endpoint devices is critical. These devices play a key role in collecting and processing sensitive data, ranging from personal information to proprietary business insights, making them high-value targets for cyberattacks. To protect AI devices from a variety of potential threats, such as malware, data breaches, and unauthorized access, it is critical to implement strong security measures. This requires the use of encryption protocols, secure boot mechanisms, and hardware-based security features to ensure protection during data transmission and at-rest storage.

The rise of edge artificial intelligence brings new opportunities for creativity, innovation and personalized experiences. However, to realize the full potential of AI, memory technologies for inference and securing edge devices must continue to evolve.

Rambus’ memory interface controller provides high-bandwidth, low-latency memory performance for GDDR and LPDDR to meet the needs of current and future artificial intelligence reasoning. In addition, Rambus has an extensive portfolio of security IP that enables cutting-edge security at the hardware level to protect AI applications on edge and endpoint devices.

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