October 30, 2023

With the proliferation of computationally intensive machine-learning applications, such as chatbots that perform real-time language translation, device manufacturers often incorporate specialized hardware components to rapidly move and process the massive amounts of data these systems demand.

Choosing the best design for these components, known as deep neural network accelerators, is challenging because they can have an enormous range of design options. This difficult problem becomes even thornier when a designer seeks to add cryptographic operations to keep data safe from attackers.

Now, MIT researchers have developed a search engine that can efficiently identify optimal designs for deep neural network accelerators, that preserve data security while boosting performance.

Their search tool, known as SecureLoop, is designed to consider how the addition of data encryption and authentication measures will impact the performance and energy usage of the accelerator chip. An engineer could use this tool to obtain the optimal design of an accelerator tailored to their neural network and machine-learning task.

Complete article from MIT News.