高級實現工具（包括英特爾的 OpenCL 軟體開發套件、賽靈思的 Vi

高級實現工具（包括英特爾的 OpenCL 軟體開發套件、賽靈思的 Vivado 高級綜合）和 Python-to-netlist 神經網路框架（如 DNNWeaver）使得面向 FPGA 和 ASIC 的 DNN 硬體設計過程變得更快、更簡單。這類軟體允許不熟悉硬體開發的 DNN 架構師可以相對容易地將其設計遷移到定制硬體上。同時，可重構性支持快速的設計反覆運算，使 FPGA 成為頂尖 DNN 的理想原型製作和部署設備。

結果 (英文) 1:

Advanced implementation tools (including Intel OpenCL Software Development Kit, Xilinx Vivado High Level Synthesis) and Python-to-netlist neural network framework (such as DNNWeaver) so as to face the DNN FPGA and ASIC hardware design process becomes faster, simpler. Such software allows architects are not familiar with DNN hardware development can be relatively easily migrate their designs to custom hardware. Meanwhile, reconfigurability to support rapid design repeated operation, the FPGA to become the top DNN ideal prototyping and deployment of equipment.

結果 (英文) 2:

Advanced implementation tools, including Intel's OpenCL software development kit, Syelings' Vivado Advanced Synthesis, and Python-to-netlist neural network frameworks such as DNNWeaver, make the DNN hardware design process for FPGAs and ASIC faster and simpler. This type of software allows DNN architects who are not familiar with hardware development to migrate their designs to custom hardware with relative ease. At the same time, refactorability supports rapid design iterations, making FPGAs the ideal prototyping and deployment device for top-notch DNNs.

結果 (英文) 3:

Advanced implementation tools (including Intel's OpenCL software development kit, Sirius's vivado advanced synthesis) and python to netlist neural network framework (such as dnnweaver) make the design process of DNN hardware for FPGA and ASIC faster and simpler. Such software allows DNN architects who are not familiar with hardware development to migrate their designs to custom hardware relatively easily. At the same time, reconfigurability supports rapid design iteration, making FPGA an ideal prototype and deployment device for top DNN.