In-memory computing device suitable for binary convolutional neural network computing

A binary convolution neural and computing device technology, applied in the field of integrated circuits, can solve the problems of reducing computing speed and wasting power consumption, and achieve the effects of increasing computing speed, avoiding data exchange, and reducing chip power consumption
CN111126579AActive Publication Date: 2020-05-08FUDAN UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
FUDAN UNIV
Publication Date
2020-05-08

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Abstract

The invention belongs to the technical field of integrated circuits, and particularly relates to an in-memory computing device suitable for binary convolutional neural network computing. The device comprises an in-memory computing array based on a static random access memory and used for realizing inter-vector exclusive-OR operation; a multi-input addition tree which is used for accumulating exclusive-OR results in different input channels; a storage unit is used for temporarily storing an intermediate result; the accumulator group is used for updating the intermediate result; the post-processing quantization unit is used for quantizing the high-precision accumulation result into a one-bit output characteristic value; and the control unit is used for controlling the calculation process andthe data flow direction. According to the device, the XOR operation in the binary neural network can be completed while the input data is stored, and frequent data exchange between the storage unit and the calculation unit is avoided, so that the calculation speed is improved, and the power consumption of the chip is reduced.
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Description

Technical field

[0001] The invention belongs to the technical field of integrated circuits, and in particular relates to an in-memory calculation device suitable for calculation of a binary convolutional neural network. Background technique

[0002] Today, thanks to the continuous development of deep convolutional neural networks, it is widely used in various fields such as image classification, autonomous driving, target recognition and tracking, and speech recognition. In order to pursue higher accuracy, the number and width of deep convolutional networks are increasing, and the increased amount of calculation and data storage make it unsuitable for terminal computing devices with limited computing resources and power capacity.

[0003] For the conflict between the above-mentioned deep convolutional neural network algorithm and hardware implementation, various quantization methods have emerged. Low-precision and even binary convolutional neural networks can achieve performance c...

Claims

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