Optimization of the Versatile Tensor Accelerator (VTA) Load Module in a Time-Triggered Memory Access

Embedded systems powered by artificial intelligence (AI) are widely employed in diverse domains. However, the lack of inherent predictability in existing AI accelerators poses significant challenges, especially in safety-critical applications where deterministic safety specifications are essential. Moreover, temporal unpredictability caused by memory access contention further hinders the suitability of current platforms for safety-critical tasks. To address these issues, we propose a time-triggered memory access approach for the versatile tensor accelerator (VTA) that provides temporal predictability guarantees for load data. Our research focuses on investigating the temporal predictability of Neural Network load input, weight, and operations. We introduce a time-triggered memory access mechanism that pre-fetches data from DDR memory ahead of the VTA load module request. Through extensive experimentation and analytical evaluations in the VTA runtime environment, we demonstrate the effectiveness of our time-triggered memory access concept. The results reveal an 8% performance improvement and reduced execution time while upholding strict safety specifications and predictability. These findings establish the feasibility of employing a time-triggered approach for runtime neural network prediction.