Accelerating Ai Tasks While Preserving Data Security

MIT researchers have developed a search engine called SecureLoop that can efficiently identify optimal designs for deep neural network accelerators that preserve data security while boosting performance. SecureLoop considers how the addition of data encryption and authentication measures will impact the performance and energy usage of the accelerator chip. Compared to conventional scheduling techniques that don’t consider security, SecureLoop can improve performance of accelerator designs while keeping data protected, allowing users to improve the speed and performance of demanding AI applications while ensuring sensitive user data remains safe from some types of attacks.