Method for transplanting deep learning network to FPAG platform

Abstract

The invention discloses a method for transplanting a deep learning network to an FPAG platform. According to the invention, a 1-bit quantification scheme is adopted to quantify an original model intoa binary deep learning model; the memory occupation is reduced to 1/32 of the original floating point type weight, and the weight parameter is only in the binary state, so that the binary operation can be quickly realized by the logic gate device, and the problem of insufficient DSP resources can be greatly alleviated to a certain extent.

Description

technical field [0001] The invention relates to a method for transplanting a deep learning network to an FPAG platform. Background technique [0002] Since the deep neural network learning technology was proposed in 2006, the huge potential of deep learning has attracted countless researchers and first-line engineers to continuously tap the limit of deep learning. In the past ten years, a large number of outstanding scientific research authors have creatively proposed one after another eye-catching deep learning models, constantly expanding the limits of deep learning capabilities. [0003] However, even though so many excellent model algorithms have emerged in today's deep learning field, even in many fields where traditional concepts believe that machines cannot surpass humans, deep learning networks have shown performance that is not inferior to humans. But how to apply them on a large scale to various industries has always puzzled the best developers. This is because d...

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Problems solved by technology

This is because deep learning models usually have a huge number of model parameters and complex network structures. Using such models for reasoning requires a large amount of computing and storage resources and consumes a lot of time, which is inconvenient in some scenarios that require high real-time performance. ground application

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