Source:Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural NetworksAbstract:尽管目前FPGA加速器已经展示出相比通用加速器更好的性能,但其设计空间未能完全发掘。… Terre...发表于论文阅读笔... 基于FPGA硬件的网络设计 moon发表于AI加速 FPGA虚拟化:突破次元壁的技术 老石 一种网络功能虚拟化的...
However, most of previous efforts focus on applying DNAS to GPU or CPU platforms, and its potential is less exploited on the FPGA. In this paper, we first propose a novel FPGA-based CNN accelerator. An accurate performance model of the proposed hardware design is also introduced. To improve...
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks (FPGA'15) Abstract: 尽管目前FPGA加速器已经展示出相比通用加速器更好的性能,但其设计空间未能完全发掘。一个重要问题是计算吞吐率与内存带宽不匹配,现有设计要么不能充分利用逻辑资源,要么不能充分利用存储带宽。同时,DNN应用不断增加的...
Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, the accele...
POM is an end-to-end optimizing framework on MLIR for efficient FPGA-based accelerator generation. POM has the following technical contributions:Programmability: POM provides a decoupled DSL that enables concise descriptions of functions, loops, and arrays. A rich collection of scheduling primitives is...
Figure 21: We offlfloaded convolutions in the ResNet workload to an FPGA-based accelerator. The grayed-out bars correspond to layers that could not be accelerated by the FPGA and therefore had to run on the CPU. The FPGA provided a 40x acceleration on offlfloaded convolution layers over ...
Figure 10: Rooflfline of an FPGA-based DL accelerator running ResNet inference. With latency hiding enabled by TVM, performance of the benchmarks is brought closer to the rooflfline, demonstrating higher compute and memory bandwidth effificiency. ...
2.3.3. Automated Accelerator Generation Frameworks Automated accelerator generation frameworks have significantly reduced the complexity and time required to develop efficient DNN accelerators. DNNWeaver [26] automates the generation of FPGA-based accelerators from high-level DNN models by utilizing hand-opt...
Figure 5: Example schedule transformations that optimize a matrix multiplication on a specialized accelerator. 数据布局优化,可以使用更好的内部数据布局,转换计算图形,在目标硬件上执行图形。先指定每个算子的首选数据布局,给定内存层次结构指定的约束。然后,如果生产者和消费者的首选数据布局不匹配,将在两者间执行适...
Optimizing OpenCL-Based CNN Design on FPGA with Comprehensive Design Space Exploration and Collaborative Performance Modeling 文章的目的是:对于给定的CNN模型,通过作者自己设计的框架对设计空间进行探索,找到一个高效的FPGA设计。这个框架包含三部分:LoopTree:在不写源代码的情况下,捕获CNN在FPGA上的硬件结构设计细节...