
Intel Corporation
5AGXMB7G4F35C4N
5AGXMB7G4F35C4N ECAD Model
5AGXMB7G4F35C4N Attributes
Type | Description | Select |
---|---|---|
Mfr | Intel | |
Series | Arria V GX | |
Package | Tray | |
Number of LABs/CLBs | 23780 | |
Number of Logic Elements/Cells | 504000 | |
Total RAM Bits | 27695104 | |
Number of I/O | 544 | |
Voltage - Supply | 1.07V ~ 1.13V | |
Mounting Type | Surface Mount | |
Operating Temperature | 0°C ~ 85°C (TJ) | |
Package / Case | 1152-BBGA, FCBGA Exposed Pad | |
Supplier Device Package | 1152-FBGA (35x35) | |
Base Product Number | 5AGXMB7 |
5AGXMB7G4F35C4N Datasheet Download
5AGXMB7G4F35C4N Overview
The chip model 5AGXMB7G4F35C4N is a high-performance, low-power integrated circuit used in a wide range of applications. It is suitable for use in high-performance digital signal processing, embedded processing, image processing and other applications that require the use of HDL language. With its low power consumption, high speed and low cost, it has become an increasingly popular choice for many applications.
In terms of industry trends, the chip model 5AGXMB7G4F35C4N is part of the larger trend of increased integration of digital signal processing and embedded processing into a single chip. This trend is driven by the need for greater efficiency and performance in digital systems. As such, the chip model 5AGXMB7G4F35C4N is likely to remain an important component in the design of many digital systems, and its use is likely to increase in the future.
Whether the chip model 5AGXMB7G4F35C4N can be applied to the development and popularization of future intelligent robots depends largely on the specific technologies needed. For example, it may be necessary to use advanced machine learning algorithms and other technologies to enable robots to interact with their environment. In such cases, the chip model 5AGXMB7G4F35C4N may be used to enable the efficient processing of the data needed for the algorithms.
In order to use the chip model 5AGXMB7G4F35C4N effectively, it is necessary to have a good understanding of the HDL language and the capabilities of the chip model. This requires technical knowledge and expertise in digital signal processing, embedded processing and other related areas. In addition, it is important to have an understanding of the various technologies that may be needed to enable the development and popularization of future intelligent robots.
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