Early-Exit DNN Inference on HMPSoCs

Using Heterogeneous Multi-Processor System-on-Chips (HMPSoCs) for Deep Neural Network (DNN) inference has become commonplace in edge devices. However, reducing the DNN inference latency on resource-constrained edge devices remains a first-class constraint. Early-Exit (EE) DNNs offer variable, input-dependent inference latency, aiming to reduce average inference latency by lowering the latency of simple inputs at the cost of increased latency for complex inputs. However, the overheads introduced by exit branches reduce the potential performance gains with EE DNNs.Furthermore, existing CPU- or GPU-only implementations of EE DNN inference under-utilize the HMPSoC. To address this limitation, we propose a cooperative and parallel CPU-GPU execution approach¹ for EE DNN inference that effectively distributes computations across all HMPSoC processors, minimizing latency variations. Our approach allows EE DNNs to achieve reduced average latency on HMPSoCs relative to their static counterparts, significantly reducing average- and worst-case inference latencies and enhancing the speed-up of EE DNN compared to the best single-processor inference. On average, the worst-case inference latency decreased by 24.8% across three commonly used EE DNNs, providing latency comparable to a static model without compromising accuracy on an RK3399PRO HMPSoC.