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.
When compared to conventional scheduling techniques that don't consider security, SecureLoop can improve performance of accelerator designs while keeping data protected. A deep neural network accelerator is a processor with an array of computational units that parallelizes operations, like multiplication, in each layer of the network. The accelerator schedule describes how data are moved and processed.
But the sizes of authentication blocks and tiles of data don't match up, so there could be multiple tiles in one block, or a tile could be split between two blocks. The accelerator can't arbitrarily grab a fraction of an authentication block, so it may end up grabbing extra data, which uses additional energy and slows down computation.
Finally, they incorporated a heuristic technique that ensures SecureLoop identifies a schedule which maximizes the performance of the entire deep neural network, rather than only a single layer.
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