Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators

Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended AI workload. To facilitate this, we present an automated generation approach for fast performance models to accurately estimate the latency of a DNN mapped onto systematically modeled and concisely described accelerator architectures. Using our accelerator architecture description method, we modeled representative DNN accelerators such as Gemmini, UltraTrail, Plasticine-derived, and a parameterizable systolic array. Together with DNN mappings for those modeled architectures, we perform a combined DNN/hardware dependency graph analysis, which enables us, in the best case, to evaluate only 154 loop kernel iterations to estimate the performance for 4.19 billion instructions achieving a significant speedup. We outperform regression and analytical models in terms of mean absolute percentage error (MAPE) compared to simulation results, while being several magnitudes faster than an RTL simulation.

It's all about PR -- Smart Benchmarking AI Accelerators using Performance Representatives

Statistical models are widely used to estimate the performance of commercial off-the-shelf (COTS) AI hardware accelerators. However, training of statistical performance models often requires vast amounts of data, leading to a significant time investment and can be difficult in case of limited hardware availability. To alleviate this problem, we propose a novel performance modeling methodology that significantly reduces the number of training samples while maintaining good accuracy. Our approach leverages knowledge of the target hardware architecture and initial parameter sweeps to identify a set of Performance Representatives (PR) for deep neural network (DNN) layers. These PRs are then used for benchmarking, building a statistical performance model, and making estimations. This targeted approach drastically reduces the number of training samples needed, opposed to random sampling, to achieve a better estimation accuracy. We achieve a Mean Absolute Percentage Error (MAPE) of as low as 0.02% for single-layer estimations and 0.68% for whole DNN estimations with less than 10000 training samples. The results demonstrate the superiority of our method for single-layer estimations compared to models trained with randomly sampled datasets of the same size.