CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to Korean Patent Application No. 10-2019-0045745 filed on Apr. 18, 2019, which is incorporated herein by reference in its entirety.

BACKGROUND

1. Field

Various embodiments relate to a processing system.

2. Discussion of the Related Art

A convolution neural network (CNN) is a processing system mainly used for image recognition and analysis. The processing system performs a convolution operation of extracting features from an image through a specific filter.

Recently, a binarized neutral network (BNN) has emerged. The BNN aims at simplifying a structure of the processing system and reducing power consumption by using two weights such as binary (using only 1 and −1 as weights) or three weights such as ternary (using only 1, 0, and −1 as weights).

SUMMARY

Various embodiments are directed to a technology of simplifying a structure of a processing system and reducing current consumption.

In an embodiment, a processing element may include: a pre-processing circuit configured to receive and filter a feature and a weight and output a filtered feature and a filtered weight; and an accumulation circuit configured to accumulate a value of the filtered feature, wherein the pre-processing circuit comprises: a sign conversion circuit configured to change a sign of the feature and a sign of the weight when the weight having a value of −1; and a zero filtering circuit configured to change a value of the weight to 0 when the feature having a value of 0.

In another embodiment, a pre-processing circuit configured to receive and filter a feature and a weight and output a filtered feature and a filtered weight; and an accumulation circuit configured to accumulate a value of the filtered feature, wherein the pre-processing circuit comprises: a sign conversion circuit configured to change a sign of the feature and a sign of the weight when the weight having a value of 1; and a zero filtering circuit configured to change a value of the weight to 0 when the feature having a value of 0.

In an embodiment, a processing system may include: a cache memory configured to store a feature and a weight; and a processing element configured to operate the feature and the weight transferred from the cache memory, wherein the processing element comprises: a pre-processing circuit configured to receive and filter the feature and the weight and output a filtered feature and a filtered weight; and an accumulation circuit configured to accumulate a value of the filtered feature, and the pre-processing circuit comprises: a sign conversion circuit configured to change a sign of the feature and a sign of the weight when the weight having a value of −1; and a zero filtering circuit configured to change a value of the weight to 0 when the feature having a value of 0.

In another embodiment, a processing system may include: a cache memory configured to store a feature and a weight; and a processing element configured to operate the feature and the weight transferred from the cache memory, wherein the processing element comprises: a pre-processing circuit configured to receive and filter the feature and the weight and output a filtered feature and a filtered weight; and an accumulation circuit configured to accumulate a value of the filtered feature, and the pre-processing circuit comprises: a sign conversion circuit configured to change a sign of the feature and a sign of the weight when the weight having a value of 1; and a zero filtering circuit configured to change a value of the weight to 0 when the feature having a value of 0.

In an embodiment, a processing element may include: a conversion circuit suitable for receiving input features and weights associated with image data, detecting whether each of the input features and weights has a first value, and selectively converting corresponding values of the input features and weights based on the detecting result to output converted features and weights; a filtering circuit suitable for receiving the converted features and weights, determining whether each of the converted features and weights has a second value, and filtering corresponding values of the converted features and weights based on the detecting result to output filtered features and weights; and an adder suitable for receiving the filtered features including a current filtered feature and a previous filtered feature, and adding the current filtered feature and the previous filtered feature based on a value of a corresponding filtered feature among the filtered weights, wherein each of the input features and weights has one of three values, and each of the converted features and weights has one of two values.

According to the embodiments, it is possible to simplify a structure of a processing system and reduce current consumption.

