Approximate calculation device and method suitable for neural network data and weight pre-classification

Abstract

According to the approximate calculation device and method suitable for neural network data and weight pre-classification, weight parameters of a neural network are pre-processed, fine classification is conducted according to the number of continuous' 0 'from the lowest bit, simple classification is conducted on input data according to the same features, and the two parties are combined to serve as an important basis for configuring an approximate scheme; approximate calculation arrays with different approximation degrees are configured by controlling approximate line positions of the full adder adder tree accumulation circuit and the low-order or approximate adder accumulation circuit. Aiming at the characteristic that weight parameters of a neural network model are often known and fixed, fine pre-classification processing is performed on the weight parameters, fine simple dichotomy processing is performed according to the characteristics of input system data, and approximate multiplication operation arrays with different configurations are dynamically selected by combining the fine pre-classification processing and the dichotomy processing. The selection of the approximation scheme is accurate, the characteristics of the corresponding data and weight are matched in real time, and the blindness, hysteresis and complexity of the dynamic configuration scheme of the existing approximation multiplication are overcome.

Description

technical field [0001] The present invention relates to the technical field of dynamic configuration approximate multiplication calculation, in particular to an approximate calculation device and method suitable for neural network data and weight pre-classification. Background technique [0002] In recent years, people pay more and more attention to energy-saving design. This is because a large number of applications require low-power custom designs, but the amount of data that these hardware needs to process has increased dramatically. In order to meet these two requirements at the same time, approximate computing has become A solution that works. [0003] With the increasing popularity of applications related to human perception, such as neural networks and machine learning, the amount of calculation of on-chip hardware is gradually increasing, but the accuracy does not require complete accuracy and correctness. This is because neural networks have natural Fault tolerance...

AI Technical Summary

Problems solved by technology

[0005] There are two existing approximate multiplication dynamic configuration schemes: one method is to control the calculation accuracy by configuring the number of iterations, which requires repeated iterations, takes a long time to configure, and cannot guarantee the real-time performance of the calculation; the other method is based on the data bit width Configure the approximate number of digits, but the adjustable range and results are blind

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