论文标题
Dragon（可区分的图形执行）：现代AI/非AI工作负载的一套硬件仿真和优化工具
DRAGON (Differentiable Graph Execution) : A suite of Hardware Simulation and Optimization tools for Modern AI/Non-AI Workloads
论文作者
论文摘要
我们介绍了Dragon，这是一种快速且可解释的硬件仿真和优化工具链，使硬件架构师能够模拟硬件设计，并优化硬件设计以有效地执行工作负载。 龙工具链提供以下工具：硬件模型生成器（DGEN），硬件模拟器（DSIM）和硬件优化器（DOPT）。 DSIM提供了所述硬件上的运行算法（表示为数据流图）的模拟。 DGEN用用户输入体系结构/技术（以自定义说明语言表示）详细描述了硬件。来自模拟的梯度下降的新方法使我们可以优化硬件模型（提供了DOPT提供的技术参数和设计参数的改进方向）。 Dragon Framework（DSIM）比以前可用的仿真作品要快得多，这是通过性能优先的代码写作实践，用于常见计算操作的数学公式，以避免避免循环精确的模拟步骤，用于映射的有效算法以及用于硬件状态的数据结构表示。 Dragon Framework（DOPT）为AI和非AI工作负载生成了性能优化的架构，并为100X-1000X更好的未来计算系统提供技术改进方向。
We introduce DRAGON, a fast and explainable hardware simulation and optimization toolchain that enables hardware architects to simulate hardware designs, and to optimize hardware designs to efficiently execute workloads. The DRAGON toolchain provides the following tools: Hardware Model Generator (DGen), Hardware Simulator (DSim) and Hardware Optimizer (DOpt). DSim provides the simulation of running algorithms (represented as data-flow graphs) on hardware described. DGen describes the hardware in detail, with user input architectures/technology (represented in a custom description language). A novel methodology of gradient descent from the simulation allows us optimize the hardware model (giving the directions for improvements in technology parameters and design parameters), provided by Dopt. DRAGON framework (DSim) is much faster than previously avaible works for simulation, which is possible through performance-first code writing practices, mathematical formulas for common computing operations to avoid cycle-accurate simulation steps, efficient algorithms for mapping, and data-structure representations for hardware state. DRAGON framework (Dopt) generates performance optimized architectures for both AI and Non-AI Workloads, and provides technology improvement directions for 100x-1000x better future computing systems.
