Dataflow architectures are growing in popularity due to their potential to mitigate the challenges posed by the memory wall inherent to the Von Neumann architecture. At the same time, high-level synthesis (HLS) has demonstrated its efficacy as a design methodology for generating efficient dataflow architectures within a short development cycle. However, existing HLS tools rely on developers to explore the vast dataflow design space, ultimately leading to suboptimal designs. This phenomenon is especially concerning as the size of the HLS design grows. To tackle these challenges, we introduce HIDA, a new scalable and hierarchical HLS framework that can systematically convert an algorithmic description into a dataflow implementation on hardware. We first propose a collection of efficient and versatile dataflow representations for modeling the hierarchical dataflow structure. Capitalizing on these representations, we develop an automated optimizer that decomposes the dataflow optimization problem into multiple levels based on the inherent dataflow hierarchy. Using FPGAs as an evaluation platform, working with a set of neural networks modeled in PyTorch, HIDA achieves up to 8.54$\times$ higher throughput compared to the state-of-the-art (SOTA) HLS optimization tool. Furthermore, despite being fully automated and able to handle various applications, HIDA achieves 1.29$\times$ higher throughput over the SOTA RTL-based neural network accelerators on an FPGA.
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
School of Microelectronics, BDBC, Beihang University（北京航空航天大学微电子学院，大数据科学与脑机智能高精尖创新中心）；School of Computer Science and Engineering, BDBC, Beihang University（北京航空航天大学计算机科学与工程学院，大数据科学与脑机智能高精尖创新中心）；Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
Georgia Institute of Technology（佐治亚理工学院）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；IBM Research（IBM研究院）；NVIDIA（英伟达）；Sandia National Laboratories（桑迪亚国家实验室）
