Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources.
翻译:深神经网络(DNN)显示,在广泛的应用中,例如图像识别、物体探测、机器人和自然语言处理,其常规机器学习算法优于常规机器学习算法,这些应用包括图像识别、物体探测、机器人和自然语言处理等。然而,由于DNN的计算复杂程度高,往往需要极其快速和高效的硬件加速器。由于神经网络的规模成倍增长,问题变得更加严重。因此,开发了定制硬件加速器,以加速DNN的处理,同时又不牺牲模型的准确性。然而,以前的加速器设计研究没有充分考虑到目标应用的特点,这些特性可能导致亚最佳建筑设计设计设计设计。另一方面,开发了新的DNNNN模型是为了提高准确性,但与基本硬件加速器的兼容性往往被忽视。我们在此篇文章中提议了一个设计 DNNNE 空间空间探索器的应用程序驱动框架。这个框架以单个 DNNNE 操作的硬件分析模型为基础,它也模拟了适应性结构设计任务,作为多维的优化。我们证明,它可以精确地改进核心应用的网络设计框架。