Development of AIoT Chips and Leading Companies

Development of AIoT Chips and Leading Companies

Jun 2020 Market Intelligence & Consulting Institute Semiconductors22 Pages Price :
$ 1400

Over the years, with the PC technology growing into popularity and maturity, the convergence of AI (Artificial Intelligence) and IoT (Internet of Things), collectively called AIoT (Artificial Intelligence of Things), has become the next area of focus. With an increasing number of IoT applications adopted in people’s everyday lives, individual industries have been connecting physical and virtual space using IoT technology while using sensors to acquire data. The acquired data is then computed and processed further using AI chips to ensure better and faster operations. This report provides an overview of AI chips in support of IoT, looks into deployment strategies of leading companies such as Intel, NVidia, MediaTek, and Qualcomm, and their solutions, as well as major players of Taiwan’s semiconductor industry in AIoT; examines future trends of AIoT chips.

Table of Contents

1.Development of AIoT 

1.1 Scenario Analysis Strengthened to Improve UX 
1.2 AIoT Increases the Demand for Edging Computer 

2. Deployment Strategies of Major AIoT Chipmakers 
2.1 Intel 
2.1.1 Hybrid Processor Architecture to Compensate the Disadvantage of CPU Parallel Computing 
2.2 NVidia 
2.3 MediaTek 
2.4 Qualcomm 

3.Deployment Strategies of Taiwan’s Semicondcutor Industry in AIoT 
3.1 AI on Chip Taiwan Alliance Established in Response to AIoT Trends 

4. MIC Perspective 


List of Companies 

List of Table

List of Tables

Table 1 Taiwanese IC Design Companies with AI Investments 
Table 2 AITA (AI on Chip Taiwan Alliance) and its Members 

List of Chart

List of Figures

Figure 1 Global IoT Connected Devices, 2018-2023 
Figure 2 AIOT Features of Amazon Alexa 
Figure 3 Computing Architecture of Amazon Alexa 
Figure 4 Illustration of an Embedded IoT Device with Intel Movidius 
Figure 5 Server Platform of Nvidia HGX-2 
Figure 6 Qualcomm Cloud AI 100 Designed for Processing High Efficient Inference Workloads 

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