阅读基于 FPGA 的 AI 推理在边缘和定制化 AI 应用中的新兴用例,以及面向边缘 FPGA AI 的英特尔® 软件和硬件解决方案。 阅读白皮书 › FPGA 与 GPU 在深度学习方面的比较 虽然不存在一种普遍适用于所有机器和深度学习应用的基础架构,但 FPGA 可以提供远远超越 GPU 和其他类型硬件的优势。 阅读文章 > 面向...
We do not see FPGA's broadly deployed for the "training" application. We do expect that NVIDIA will continue to have significant competition in training - it has been a major focus of Intel through it's Xeon PHI lineup, and is starting to emerge as a priority for AMD. But NVIDIA's 1...
Consume less power: With FPGAs, designers can fine-tune the hardware to the application, helping meet power efficiency requirements. FPGAs can also accommodate multiple functions, delivering more energy efficiency from the chip. It’s possible to use a portion of an FPGA for a function, rather...
GPUs, and ASICs for acceleration, and CPUs for general-purpose computing. Each architecture serves unique needs, so infrastructure architects can choose the exact architecture they need to support any AI application. With a breadth of compute types optimized for power and performance, they’ll always...
于是，越来越多的企业，开始加强对专用计算芯片的研究和投资力度。而ASIC（Application Specific Integrated Circuit，专用集成电路），就是一种专用于特定任务的芯片。ASIC的官方定义，是指：应特定用户的要求，或特定电子系统的需要，专门设计、制造的集成电路。ASIC起步于上世纪70-80年代。早期的时候，曾用于计算机。
ASIC ASIC（Application-Specific Integrated Circuit , 应用特定集成电路）是专门设计用于特定应用的芯片。它们被广泛用于加速 AI 工作负载，因为它们可以实现高度定制化的计算，具有极高的性能,与通用集成电路不同，ASIC电路是根据特定的应用要求进行设计和定制的，其功能非常专一。ASIC一般用于需要高度可靠性、高速度和...
While general-purpose GPUs cannot be reprogrammed, the FPGA’s reconfigurability allows for specific application optimization, leading to reducedlatencyand power consumption. This key difference makes FPGAs particularly useful for real-time processing in AI applications and prototyping new projects. ...
ASIC（Application-Specific Integrated Circuit）即应用特定集成电路，是一种为特定应用设计的定制芯片。与FPGA不同，ASIC是硬固定的，无法像FPGA那样重新配置。但ASIC的效率远高于FPGA，因为它的所有部分都是为特定应用设计的。ASIC在许多领域都有应用，包括通信、工业控制、加密等。总结起来，FPGA、CPU、GPU、NPU和ASIC...
Their Brainwave project provides FPGA technology for accelerating deep neural network inference. They, like Alibaba Cloud, use Intel’s Stratix 10 FPGA. While Intel leads the FPGA industry in the AI application acceleration space, Xilinx, another prominent FPGA maker, plans to enter the fray. Xilin...
The FPGA AI Suite enables FPGA designers, machine learning engineers, and software developers to create optimized FPGA AI platforms efficiently. Utilities in the suite speed up FPGA development for AI inference using familiar and popular industry frameworks such as TensorFlow or PyTorch and OpenVINO ...