边缘AI加速器-新商机分析

Edge AI Accelerators-Emerging Opportunity Analysis

出版日期: 2023年09月20日 | 出版商:

Frost & Sullivan | 英文 53 Pages | 商品交期: 最快1-2个工作天内

价格

简介目录

扩大物联网应用推动成长

对即时深度学习工作负载的需求不断增长，使得专用边缘人工智慧硬体对于实现快速设备上深度学习至关重要。此外，云端基础的人工智慧方法无法确保资料隐私、低延迟和高频宽。因此，许多人工智慧工作负载正在转移到边缘，增加了对专门用于设备上机器学习推理的人工智慧硬体的需求。

物联网的发展、消费性电子和汽车产业对智慧技术的采用以及智慧工业自动化正在推动边缘人工智慧加速器市场的发展。适用于智慧型手机、穿戴式装置和智慧家电等消费性应用的人工智慧加速器不仅需要小型化，还需要高处理成本比。同时，高处理速度和功效是许多工业和企业应用中使用的人工智慧加速器最重要的要求。

大多数晶片製造商都在努力提高处理速度，同时降低功耗。为了克服这个问题，公司正在投资开发用途晶片、高效能晶片架构、新演算法、先进记忆体和替代材料。为了利用这些技术进步，领先的公司正在采取联盟和收购等技术策略。

预计美国、韩国、中国、日本、德国和以色列的边缘人工智慧加速器市场将显着成长。这是由于与家用电器、汽车、工业设备和国防相关的製造活动量很大。这些国家除了拥有强大的製造基础外，还建立了强大的晶片製造生态系统，这对于维持市场主导地位至关重要。

深度学习、神经网路、电脑视觉、生成人工智慧和神经形态运算的出现正在为边缘推理应用创造新的机会。随着公司迅速转向分散式电脑架构，他们正在学习应用该技术来提高生产力和降低成本的新方法。因此，人工智慧晶片开发人员需要更专注于开发旨在满足这些使用案例特定要求的解决方案。

这份 Frost & Sullivan 研究报告涵盖以下主题：

主要人工智慧加速器技术概述和重要性
主要边缘AI处理器比较分析
新使用案例
产业企业技术趋势及主要发展策略
AI加速器晶片产业商业模式
边缘AI加速器领域区域分析
AI加速器路线图
成长机会

执行摘要
关键硬体技术-CPU概述
关键硬体技术-GPU概述
关键硬体技术 - ASIC 概述
主要边缘AI CPU、GPU和ASIC对比分析
按应用分析关键效能要素
边缘人工智慧加速器的新使用案例
融合场景提高工业环境中的员工安全
策略伙伴关係
併购
主要创新主题
主要参与者和新产品开发配合措施
针对新兴企业和新产品开发的配合措施
AI加速器晶片产业商业模式
边缘人工智慧加速器生态系统
边缘人工智慧加速器的区域分析 - 亚太地区
边缘人工智慧加速器的区域分析 - 欧洲和以色列
边缘人工智慧加速器的区域分析 - 北美
AI加速器路线图

成长机会宇宙

成长机会 1：开发特定工作负载的 AI 加速器
成长机会2：将AI晶片融入小型设备
成长机会 3：开发更快的互连

附录

技术完备等级(TRL)：说明

下一步

下一步
为什么是霜冻，为什么是现在？
免责声明

简介目录

Product Code: DAB2

Expanding IoT Applications Drive Growth

Specialized edge AI hardware that enables quick deep learning on-device has become essential due to the rising need for real-time deep learning workloads. Additionally, a cloud-based AI method cannot ensure data privacy, low latency, or offer high bandwidth. As a result, many AI workloads are shifting to the edge, increasing the demand for specialized AI hardware for on-device machine learning inference.

The growth of IoT, smart technology adoption by consumer electronics and the automotive industry, and intelligent industrial automation are propelling the edge AI accelerator market. AI accelerators in consumer-oriented applications, such as smartphones, wearables, and smart appliances, need to have a high processing-to-cost ratio as well as a smaller size. On the other hand, for most of the AI accelerators used in industrial/enterprise applications, the requirement for high processing speed and power efficiency are of prime significance.

The majority of chip manufacturers are struggling to improve processing speed while reducing power consumption. To overcome this, organizations are investing in developing application-specific chips, efficient chip architectures, new algorithms, advanced memories, and alternative materials. To leverage these technological advancements, major corporations are embracing technology strategies such as partnerships and acquisitions.

The market for edge AI accelerators is projected to grow significantly in the United States, South Korea, China, Japan, Germany, and Israel. This is due to the high amount of manufacturing activity pertaining to consumer electronics, automotive, industrial equipment, and defense. Apart from having a strong manufacturing base, these countries have also developed a strong ecosystem for chip manufacturing, which is crucial to maintaining a dominant position in the market.

The emergence of deep learning, neural networks, computer vision, generative artificial intelligence, and neuromorphic computing has created new opportunities for edge inferencing applications. While enterprises are quickly moving towards a decentralized computer architecture, they are also learning new methods to apply this technology to boost productivity and cut costs. Therefore, AI chip developers should focus more on developing solutions that are designed to fulfill these requirements specific to use cases.

This Frost & Sullivan research report covers the following topics:

Overview and significance of key AI accelerator technologies
Comparative analysis of key edge AI processors
Emerging use cases
Technology trends and key developmental strategies used by players in the industry
Business models in the AI accelerator chip industry
Regional analysis of the edge AI accelerator space
AI accelerators roadmap
Growth opportunities

Strategic Imperatives

Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
The Strategic Imperative 8™
The Impact of the Top 3 Strategic Imperatives of Edge AI Accelerators Industry
Growth Opportunities Fuel the Growth Pipeline Engine™
Research Methodology