对CNN FPGA加速器的技术 (例如循环平铺和转换) 优化,同时进行了定量分析计算吞吐量和片内外I/0带宽和建模 通过roof-line模型搜索加速器硬件参数设计空间中最优的方案, 最后通过此建模方案设计了一个加速器,获得当时最优性能密度的CNN加速器。 背景与动机 回答Paper 背景和解决什么问题? 背景 卷积神经网络 (CNN) ...
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...
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 accuracy as well as hardware performance, we then apply DNAS and encapsulate the proposed performance model into the objective function....
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...
•构建了一个端到端的编译和优化堆栈,在高级框架(包括TensorFlow,MXNet,PyTorch,Keras,CNTK)中,多种硬件后端(包括CPU,服务器GPU,移动GPU和基于FPGA的加速算子)特定的工作负荷,部署深度学习。 开源TVM在几家大公司内部量产使用。在服务器级GPU,嵌入式GPU,嵌入式CPU和一个定制的基于FPGA的通用加速器上,使用真实的工...
SIDH Accelerator: The SIDH accelerator is responsible for performing the isogeny-based operations at the heart of SIKE. These operations are computationally intensive, and the accelerator is designed to execute them efficiently by leveraging the parallelism inherent in FPGA architecture. 4.5.2 SIDH acce...