VisionAGILE: A Versatile Domain-Specific Accelerator for Computer Vision Tasks

Abstract

The emergence of diverse machine learning (ML) models has led to groundbreaking revolutions in computer vision (CV). These ML models include convolutional neural networks (CNNs), graph neural networks (GNNs), and vision transformers (ViTs). However, existing hardware accelerators designed for CV lack the versatility to support various ML models, potentially limiting their applicability to real-world scenarios. To address this limitation, we introduce VisionAGILE, a domain-specific accelerator designed to be versatile and capable of accommodating a range of ML models, including CNNs, GNNs, and ViTs. VisionAGILE comprises a compiler, a runtime system, and a hardware accelerator. For the hardware accelerator, we develop a novel unified architecture with a flexible data path and memory organization to support the computation primitives in various ML models. Regarding the compiler design, we develop a unified compilation workflow that maps various ML models to the proposed hardware accelerator. The runtime system executes dynamic sparsity exploitation to reduce inference latency and dynamic task scheduling for workload balance. The compiler, the runtime system, and the hardware accelerator work synergistically to support a variety of ML models in CV, enabling low-latency inference. We deploy the hardware accelerator on a state-of-the-art data center FPGA (Xilinx Alveo U250). We evaluate VisionAGILE on diverse ML models for CV, including CNNs, GNNs, hybrid models (comprising both CNN and GNN), and ViTs. The experimental results indicate that, compared with state-of-the-art CPU (GPU) implementations, VisionAGILE achieves a speedup of $81.7\times$ ( $4.8\times$ ) in terms of latency. Evaluated on standalone CNNs, GNNs, and ViTs, VisionAGILE demonstrates comparable or higher performance with state-of-the-art CNN accelerators, GNN accelerators, and ViT accelerators, respectively.