ApNet: Approximation-Aware Real-Time Neural Network

Abstract

Modern embedded cyber-physical systems are becoming entangled with the realm of deep neural networks (DNNs) towards increased autonomy. While applying DNNs can significantly improve the accuracy in making autonomous control decisions, a significant challenge is that DNNs are designed and developed on advanced hardware (e.g., GPU clusters), and will not easily meet strict timing requirements if deployed in a resource-constrained embedded computing environment. One interesting characteristic of DNNs is approximation, which can be used to satisfy real-time requirements by reducing DNNs' execution costs with reasonably sacrificed accuracy. In this paper, we propose ApNet, a timing-predictable runtime system that is able to guarantee deadlines of DNN workloads via efficient approximation. Rather than straightforwardly approximating DNNs, ApNet develops a DNN layer-aware approximation approach that smartly explores the trade-off between the approximation degree and the resulting execution reduction on a per-layer basis. To further reduce approximation-induced accuracy loss at runtime, ApNet explores a rather interesting observation that resource sharing and approximation can mutually supplement one another, particularly in a multi-tasking environment. We have implemented and extensively evaluated ApNet on a mix of 8 different DNN configurations on an NVIDIA Jetson TX2. Experimental results show that ApNet can guarantee timing predictability (i.e., meeting all deadlines), while incurring a reasonable accuracy loss. Moreover, accuracy can be improved by up to 8% via a resource sharing increase of 3.5x on average for overlapping DNN layers.