Abstract
Encryption of streaming video is becoming critical to the success of commercial enterprise and to consumers alike. To meet copyright and privacy requirements, encrypting video data on-the-fly during transmission is necessary. In this work we employ Advanced Encryption Standard (AES) due to its security and flexibility. However, it is compute and memory-intensive. Increasing the number of concurrent streamers only exacerbates the problem. To ameliorate this situation, we propose a Processing-In Memory (PIM) architecture AESPIM to offload AES encryption computation to the memory side. Based on our experimental results, AES performance can be improved up to 42% with compared with the traditional CPU based implementation by significantly reducing data movement and increased memory bandwidth. We further study the data-level and user-level parallelism of the video streaming environment in our base design AESPIM architecture, and propose an advanced design based on user-level parallelism to achieve additional 34% performance improvement. Furthermore, we implement a QoS-aware scheduler to solve its potential workload imbalance bottleneck. Based on evaluation results, performance can be further optimized by up to 6% on average.
Published Version
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