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 design provides 1.75x higher solution-level performance for INT8 deep learning operations, making it a top choice for resource-constrained environments.
This white paper delves into the use of INT8 operations in embedded vision systems running on Xilinx DSP48E2 slices. It also compares these implementations with those found in other FPGAs. Xilinx’s architecture achieves 1.75x peak performance for INT8 multiply-accumulate (MACC) operations when using the same amount of resources as competing solutions. The reason behind this is that many embedded vision tasks can benefit from lower bit precision without compromising accuracy, which makes an efficient INT8 implementation essential.
The Xilinx DSP architecture and its associated libraries are specifically optimized for INT8 operations. This document explains how to utilize the DSP48E2 slice in Xilinx 16nm and 20nm All Programmable devices to perform two parallel INT8 MACC operations while sharing the same weight matrix. It also discusses the significance of the minimum 24-bit input width enabled by Xilinx technology. Additionally, it covers how the DSP48E2 slice can be used in SIMD mode for basic arithmetic operations, providing examples of how these features can be applied in real-world embedded vision scenarios such as deep learning and computer vision processing.
**Book Catalog:**
- INT8 for Deep Learning and Computer Vision
- INT8 Operation on Xilinx DSP Slice Slices
- Scalable INT8 Optimization
- DSP48E2 SIMD Mode
- Mapping INT8 Optimization to Deep Learning Applications
- Alternative Approaches for Creating an INT8 MACC
- Mapping INT8 Optimization to Computer Vision
- Custom 2D Convolution with Scalable INT8 Optimization
- Median Filter Using SIMD Operations
**Competitive Analysis:**
This section compares Intel’s Arria 10 devices with Xilinx’s Zynq® UltraScale+™ MPSoC. Both device families offer comparable DSP density and power consumption, making them suitable for embedded vision applications. The comparison includes:
- Arria 10 SoC: SX220, SX270, and SX480
- Zynq UltraScale+ MPSoC: ZU3, ZU7, and ZU9
The focus is on general-purpose MACC performance across a wide range of applications, including deep learning and traditional computer vision tasks. By leveraging Xilinx’s optimized INT8 architecture, developers can achieve better efficiency and performance in their embedded vision projects.
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