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The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and ...
摘要: Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools. This paper identifies trends in deep learning research that will influence hardware architectures and software platforms of the future.关键词:...
such as device variability, can lead to trade-offs between the accuracy and fairness of AI models. Their findings highlight the need to carefully consider both the design of AI model structures and the hardware platforms they will be deployed on, to reach a good balance between accuracy ...
相比之下,低阶IR则更加specific,对其的优化更加关注于hardware platforms本身的架构设计。那么为什么能叫glow,glow其实是graph+lowering的简写,意思就是说,通过这个低阶的IR,从而在针对大量不同的上层model中的op到下层不同hardware accelerator的实现都尽可能通过一些比较简单的线性代数源语来实现,有点类似精简指令集的...
researchers from Harvard University came up with one suchidea of developing a systematic and scientific approach to platform benchmarking. According to the researchers the benchmarking should not only compare the performance of different platforms which is running a broad range of deep learning models,...
AMD Vitis-AI quantizer. Vitis-AI quantizer is a tool provided by AMD as part of the Vitis-AI development platform. It is designed to facilitate model quantization for efficient deployment of deep learning models on AMD hardware platforms. You can easily set Vitis-AI quantizer ...
The primary purpose of DeepBench is to benchmark operations that are important to deep learning on different hardware platforms. Although the fundamental computations behind deep learning are well understood, the way they are used in practice can be surprisingly diverse. For example, a matrix multipli...
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Deploy your solutions across hardware platforms with the power and performance required for deep learning and inference at the edge. These hardware and devices are lab tested to deliver optimized power and performance when used with Intel software packages. Original device and equipment manufacturers (...