RDP3: Rapid Domain Platform Performance Prediction for Design Space Exploration

Abstract

Heterogeneous Accelerator-rich (ACC-rich) platforms combining general-purpose cores and specialized HW Accelerators (ACCs) promise high-performance and low-power deployment of streaming applications, e.g. for video analytics, software-defined radio, and radar. In order to recover Non-Recurring Engineering (NRE) cost, a unified domain platform for a set of applications can be exploited, especially when applications have functional and structural similarities, which can benefit from common ACCs. However, identifying the most beneficial set of common ACCs is challenging, and current Design Space Exploration (DSE) methods for domain platform allocation suffer from a long exploration time bottleneck. In particular, compared to a traditional DSE, evaluating the performance of a platform for a domain of applications is much more time-consuming as binding exploration and evaluation for each application in the domain is required. Thus, a rapid domain performance evaluation is needed to speed up the exploration of the platform allocation.This paper introduces Rapid Domain Platform Performance Prediction (RDP3) methods to speed up the exploration in domain DSE. Key contributions are: (1) analyzing current domain DSE flow and its exploration time bottleneck; (2) introducing four RDP3 methods to speedup the evaluation of different platform allocations: Heuristic Processing (HP) estimation, Linear Regression (LR), Decision Tree Regression (DTR), and Multi-Layer Perceptron (MLP) predictions; (3) comparing the performance of these predictions and integrating the prediction into the current domain DSE. To evaluate the efficacy of RDP3, we explore 10K platforms capable of processing OpenVX domain applications. We demonstrate that RDP3-MLP as the most promising method can achieve a speedup of 17.5K times with only 0.001 mean square error compared to the current platform evaluation using the analytical model. Integrating RDP3-MLP into the existing domain DSE method GIDE [1] can save 80.8% exploration time while still resulting in the same output platform design.