Unveiling the Phantom v5 Camera: A Deep Dive into 2001 Technology

Figure 1: 2001 Phantom v5 Video Camera

The Phantom v5 is a high-speed, high-resolution camera launched by Vision Research in 2001, capable of capturing video at 1024×1024 pixels and 1000 frames per second. It has since been discontinued. Today, we will explore its internal workings through disassembly.

Upon disassembly, we discovered that the timing and interface module board utilizes a pair of Xilinx XC4020 FPGA devices, while the high-speed acquisition and storage board employs Xilinx XC9500 CPLD devices for timing and control functions, integrating a Hitachi SH-2 32-bit RISC processor for communication with external devices.

The Xilinx XC4020 FPGA device, released in 2000, belongs to the third generation of FPGAs. Each device integrates 784 CLB units, equivalent to 1560 LTUs, which was considered resource-rich at the time, but now appears quite limited. Even the lowest specification single-core Zynq Z-7007S SoC (integrating an ARM Cortex-A9 processor) could completely replace the XC4020 and SH-2 processors, offering at least a 20-fold performance increase.

Regarding acquisition speed, the Phantom v5 could achieve 1000 frames per second at the time, while Vision Research’s latest products now support 1 million frames per second, a 1000-fold increase over 17 years! Of course, these antique devices can no longer meet the demands of modern applications. Xilinx’s All Programmable devices continue to provide technical support and innovation in video acquisition and visual analysis, with the latest reVISION stack extending fully programmable technology to a wide range of vision-oriented machine learning applications.

Figure 2: The Emergence of Xilinx reVISION Technology Stack

The Xilinx reVISION stack includes a wealth of development resources for platform, algorithm, and application development, supporting the most popular neural networks (such as AlexNet, GoogLeNet, SqueezeNet, SSD, and FCN) as well as library elements (such as predefined optimized implementations of CNN network layers, essential for building custom neural networks DNN/CNN). Coupled with rich OpenCV functionalities to meet acceleration requirements, it supports machine vision processing.

The reVISION stack introduced by Xilinx can support a broader range of embedded software and system engineers with little or no hardware design expertise, enabling them to quickly develop vision-oriented intelligent systems in conjunction with machine learning, computer vision, and sensor fusion. Even developers without hardware expertise can create embedded vision applications using a single Zynq SoC or MPSoC chip by combining C/C++/OpenCL development processes, industry-standard frameworks, and libraries like Caffe and OpenCV.