Xilinx's INT8 optimization delivers superior performance and energy efficiency for embedded vision applications that rely on deep learning inference and traditional computer vision techniques. Compared to other FPGA-based DSP architectures, Xilinx’s integrated DSP design offers 1.75x better solution-level performance for INT8 deep learning operations. This white paper explores how INT8 operations can be effectively used in embedded vision systems, focusing on their implementation on Xilinx DSP48E2 slices and comparing them with other FPGA solutions.
The paper highlights the advantages of Xilinx’s optimized DSP architecture and libraries for INT8 operations. It explains how the DSP48E2 slice in Xilinx 16nm and 20nm All Programmable devices can perform two parallel INT8 multiply-accumulate (MACC) operations using shared weights. Additionally, it discusses why the minimum input bit width supported by Xilinx technology is 24 bits, and how the DSP48E2 slice can be used in SIMD mode for basic arithmetic functions. Practical examples are provided to demonstrate how these features can be applied in real-world deep learning and computer vision tasks.
This document also covers scalable INT8 optimization strategies, mapping INT8 techniques to deep learning applications, and alternative methods for implementing INT8 MACC units. In the context of computer vision, it presents custom 2D convolution implementations using scalable INT8 optimization and demonstrates how SIMD operations can be used for tasks like median filtering.
A competitive analysis is included, comparing Intel’s Arria 10 devices with Xilinx’s Zynq® UltraScale+™ MPSoC. The comparison focuses on computational efficiency for embedded vision applications, with devices selected based on similar DSP density and power consumption levels:
- **Arria 10 SoC**: SX220, SX270, and SX480
- **Zynq UltraScale+ MPSoC**: ZU3, ZU7, and ZU9
The goal is to evaluate general-purpose MACC performance across a wide range of applications, including both deep learning and traditional computer vision tasks.
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