Heterogeneous Dataflow Accelerators for Multi-DNN Workloads

Abstract

Emerging AI-enabled applications such as augmented/virtual reality (AR/VR) leverage multiple deep neural network (DNN) models for sub-tasks such as object detection, hand tracking, and so on. Because of the diversity of the sub-tasks, the layers within and across the DNN models are highly heterogeneous in operation and shape. Such layer heterogeneity is a challenge for a fixed dataflow accelerator (FDA) that employs a fixed dataflow on a single accelerator substrate since each layer prefers different dataflows (computation order and parallelization) and tile sizes. Reconfigurable DNN accelerators (RDAs) have been proposed to adapt their dataflows to diverse layers to address the challenge. However, the dataflow flexibility in RDAs is enabled at the area and energy costs of expensive hardware structures (switches, controller, etc.) and per-layer reconfiguration. Alternatively, this work proposes a new class of accelerators, heterogeneous dataflow accelerators (HDAs), which deploys multiple sub-accelerators each supporting a different dataflow. HDAs enable coarser-grained dataflow flexibility than RDAs with higher energy efficiency and lower area cost comparable to FDAs. To exploit such benefits, hardware resource partitioning across sub-accelerators and layer execution schedule need to be carefully optimized. Therefore, we also present Herald, which co-optimizes hardware partitioning and layer execution schedule. Using Herald on a suite of AR/VR and MLPerf workloads, we identify a promising HDA architecture, Maelstrom, which demonstrates 65.3% lower latency and 5.0% lower energy than the best FDAs and 22.0% lower energy at the cost of 20.7% higher latency than a state-of-the-art RDA. The results suggest that HDA is an alternative class of Pareto-optimal accelerators to RDA with strength in energy, which can be a better choice than RDAs depending on the use cases.