Your Next SoC Will Probably Include AI Acceleration

随着添加更专业的硬件以优化新应用程序的SOC，System-On-Chip（SOC）解决方案继续变得更加复杂。Qualcomm’s最新的Snapdragon 855(Fig. 1)突出显示了这一变化。855包括许多块，包括Snapdragon X24蜂窝调制解调器和无线Wi-Fi，蓝牙和Adreno 640 GPU的GPS支撑）。

1. Qualcomm’s Snapdragon 855 SoC includes the Adreno 640 GPU, the Hexagon 690 DSP, the octal core Kryo 485 processor cluster, and the Spectra 380 image signal processor, along with wireless communication and security support.

The 64-bit Kyro 485 includes one high-performance “Gold Prime” 2.84-GHz core with a 512-kB L2 cache, three 2.42-GHz cores with 256-kB L2 caches, and four 1.8-GHz cores with 128-kB L2 caches designed for low-power operation. The Gold Prime core targets single-threaded, user-interface applications like web browsers.

The Hexagon 690 DSP is where the most change has occurred. It includes a Tensor accelerator and new vector extensions with support for INT8 and INT16 datatypes. The enhancements highlight the machine-learning (ML) support of the 855. The Kyro 485 also includes new dot product instructions with INT8 and single precision floating point that can also be useful with ML applications. Many chip designers are enhancing the DSP with ML support since DSP architectures already lend themselves to ML applications.

855个目标是高级智能手机和其他高端应用程序，而其兄弟姐妹Snapdragon 8CX则以具有较高性能的GPU为目标。ML在图像处理和计算机视觉方面发挥作用，以获得面部识别支持。ISP旨在为ML图像应用程序提供数据。

Seth DeLand, Product Marketing Manager of Data Analytics at MathWorks, notes that “Companies will increasingly use machine learning algorithms to enable products and services to ‘learn’ from data and improve performance. Machine learning is already present in some areas: image processing and computer vision for facial recognition, price and load forecasting for energy production, predicting failures in industrial equipment, and more. In the coming year, it can be expected that machine learning will be increasingly present as more companies are inspired to integrate machine-learning algorithms into their products and services by using scalable software tools, including MATLAB.”

More Machine Learning

ML support is showing up at all levels.MediaTek’sHelio P70围绕一个八进制核心大。还有一个900 MHz ARM MALI-G72 GPU和双核AI处理单元（APU）。APU旨在实时处理诸如人类姿势识别的琐事，以及对静止图像和视频的增强。它可以提供280个GMAC。

Moving further down the scale isRenesas’RZ/A2M with DRP. It’s designed to support human machine interfaces (HMIs) including systems with cameras. It has an Arm Cortex-A9 along with Renesas’ Dynamically Reconfigurable Processor (DRP) that provides ML support. The DRP is programmable in C and has optimized DMA support to minimize data movement(Fig. 2). The DRP is designed to be reconfigured every clock cycle, allowing for implementation of innovate algorithms.

2. Renesas’ Dynamically Reconfigurable Processor includes an array of processing elements, MACs, and memory blocks. Streams of data are moved to and from main memory by the DMA controller.

ML hardware acceleration is becoming the norm. However, it’s possible to run more functionally constrained ML applications on microcontrollers like the Cortex-M7 that have no ML hardware acceleration. It helps to have DSP-style support.

Many platforms, like Renesas’ RZ family, include incarnations without ML acceleration. More often than not, though, ML will be an option available to developers. Much of this support targets image and video processing, but the hardware tends to be applicable to almost any ML inference application.