AI Must Be Secured at the Silicon Level

Find the Latest SoC Solutions
for... Automotive... IoT... Security... Audio... Video

Hardware-enabled security is essential to maintaining the integrity of valuable AI workloads.

www.eetasia.com/, Feb. 10, 2023 – 

The idea of baking security into an application isn't new in the software world, nor are security features in semiconductor technologies, such as memory. But the value of data, particularly in artificial-intelligence (AI) workloads, means hardware-enabled security is getting more attention.

Many networking and memory technologies have built-in security features – the "S" in SD card stands for secure, and SSDs have long had the ability to encrypt data. The key challenges for enabling hardware-level security features, however, are educating users on how to implement them and ensuring that security doesn't hinder performance of the device and the overall system.

Although hardware-enabled security has been around for a while, securing AI workloads is a relatively new concept, said Carl Shaw, safety and security architect at Codasip, a company that focuses on processor design automation and RISC-V processor IP.

Hardware-enabled security technologies for AI

Securing AI can be broken down into two stages: security when training the network, which is more of an IT security issue, and security at the inference stage, which occurs when executing the network, according to Shaw. "This is the part that would most likely happen on a Codasip processor performing edge AI that we need to consider."

Because AI algorithms are software, Codasip's secure boot and CPU-level security mechanisms protect any software that runs on the device from tampering and IP theft, Shaw said. "Our fault protection technology will stop any corruption of the AI network while it is executing."

Encryption, integrity protection and authentication IP will provide protection for the output from the AI model so it can be safely stored and forwarded, he added. "This is particularly important where privacy is required."

But with CPUs already being taxed by the ever-increasing amount of microservices supporting containerized and virtualized apps spread across data centers, handling security just adds more pressure. Nvidia's latest data processing unit (DPU), the BlueField-2, is used to offload, isolate, accelerate and secure data center infrastructure services so that CPUs and GPUs are free to focus on running and processing large volumes of workloads, including AI. The new DPU also reflects a trend toward hardware playing a key role in implementing zero-trust security in the data center and at the edge.

click here to read more...

 Back