Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurable DNN inference accelerators which allows for the exploration of accelerator designs and configuration space. However, preparing models for evaluation and exploring configuration space in STONNE is a manual developer-timeconsuming process, which is a barrier for research. This paper introduces Bifrost, an end-to-end framework for the evaluation and optimization of reconfigurable DNN inference accelerators. Bifrost operates as a frontend for STONNE and leverages the TVM deep learning compiler stack to parse models and automate offloading of accelerated computations. We discuss Bifrost's advantages over STONNE and other tools, and evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost introduces a module leveraging AutoTVM to efficiently explore accelerator designs and dataflow mapping space to optimize performance. This is demonstrated by tuning the MAERI architecture and generating efficient dataflow mappings for AlexNet, obtaining an average speedup of $50\times$ for the convolutional layers and $11\times$ for the fully connected layers. Our code is available at www.github.com/gicLAB/bifrost.
翻译:用于深神经网络的可重新配置加速器(DNNS), 承诺提高性能, 如推导延迟。 STONNE 是第一个周期性准确模拟器, 用于重新配置 DNN 推推加速器, 用于探索加速器设计和配置空间。 然而, 准备用于评估和探索STONNE 配置空间的模型是一个手工开发的耗时空间过程, 这是一个研究屏障。 本文介绍了Bifrost, 一个用于评估和优化可重新配置的 DNNE 加速器的终端到终端框架。 BOfrost作为StoonNE的前端操作, 利用TVM深学习编译器堆来分析加速器设计和配置空间配置空间配置空间。 我们讨论Bifrost在STONNE和其他工具上的优势, 并用Bifrost 来评估MAERI和SIGMA的架构。 此外, Bifrost 引入一个模块, 利用AutiveTVM 来高效探索可重新配置的 DELNNE 高级数据结构设计, 和通过显示的MALLADR dal droad 数据流, 优化数据流, 数据流, 数据流, 正在通过演示数据流进行数据流, 将数据流进行数据流优化, 将数据流优化到生成到数据流的图像平流,, 以生成数据结构进行数据流优化到数据流数据结构, 优化到数据流, 。