YOLO Multi-Object Detection And Classification. Image credit: Ilija Mihajlovic
For over a decade there has been a surge of interest in Artificial Intelligence (AI). This interest is largely driven by the explosion in the amount of generated data, which enables the development of accurate Machine Learning (ML) models. The generated data volumes will continue to grow at an exponential pace due to the ongoing increase in the adoption of the internet and the proliferation of the Internet of Things (IoT) devices.
At the same time, the growth of AI is propelled by the rising availability of cheap storage and computing resources. The latter facilitates the effective processing of large data volumes by ML frameworks for performing complex tasks such as automated analysis of visual scenes and other computer vision applications. Computer vision systems perceive their environment and perform actions based on visual data and are among the most prominent examples of AI.
For nearly a decade, most AI systems were cloud-based. They leveraged the capacity and scalability of cloud infrastructures towards analyzing large numbers of data points by means of computationally expensive ML algorithms. Nevertheless, cloud-based systems have proclaimed limitations for certain classes of AI applications, including most computer vision applications.
For instance, they can hardly offer real-time performance as transferring data to the cloud involves high-latency wide area networks. Also, there are cases where enterprises are not willing to share data outside their local networks for privacy and data protection reasons. Moreover, executing ML models on high-end CPUs (Central Processing Units) and GPUs (Graph Processing Units) requires significant compute cycles and exhibits poor environmental performance.
Power efficiency is a very important requirement for computer vision systems, which are very computationally intensive. This is also the reason why computer vision systems are usually benchmarked in terms of their energy performance, which is typically measured in Frames Per Second Per Watt (FPS/Watt).
In recent years, these limitations of cloud AI systems are driving a shift of AI functions from cloud to edge systems. Edge AI systems collect, manage and process data close to the field i.e., within local clusters and field devices. In the case of computer vision applications, this shift has given rise to the Embedded Vision AI systems. The latter deploys and executes complex machine learning models and other AI functions within embedded devices. Embedded vision processing is a fast-growing computer vision technology, which is encapsulated in computer chips and is eventually embedded into various devices.
Embedded vision systems come with a host of benefits for both users and AI systems operations. These benefits include:
The development and deployment of embedded vision applications is not an easy task. Specifically, system developers, deployers, and operations are confronted with the following technological challenges:
Renesas offers an ecosystem of novel embedded vision AI platforms and tools, which facilitate developers and deployers to address the above-listed challenges. Specifically, Renesas offers:
Moreover, Renesas offers a complete software development environment, which runs on Personal Computers (PCs). This environment enables developers to access the functionalities of the DRP-AI translator towards converting the trained AI model to object code suitable for the DRP-AI accelerator.
Leveraging the capabilities of the DRP-AI accelerator, Renesas solutions achieve exceptional power efficiency, which sets them apart from state-of-the-art computer vision solutions that use higher-power GPUs (Graph Processing Units). Specifically, Renesas solutions end-up performing vision AI computations using 1/3 of the power consumed by state of the art GPU-based solutions when performing the same computations. Renesas is therefore providing solutions with the higher FPS/Watt in the market.
Overall, Renesas delivers an integrated, high-performance, and power-efficient embedded vision AI solution, which meets stringent application requirements and real-time constraints. The combination of the DRP-AI accelerator and translator modules provides an innovative embedded vision infrastructure that goes far beyond what current AI technology can support. In this way, Renesas paves the wave for a future generation of high-performance, energy-efficient and cost-effective computer vision applications at the edge of the network. Read more on the Renesas DRP-AI here.
At Renesas we continuously strive to drive innovation with a comprehensive portfolio of microcontrollers, analog and power devices. Our mission is to develop a safer, healthier, greener, and smarter world by providing intelligence to our four focus growth segments: Automotive, Industrial, Infrastructure, and IoT that are all vital to our daily lives, meaning our products and solutions are embedded everywhere.
John Soldatos holds a PhD in Electrical & Computer Engineering from the National Technical University of Athens (2000) and is currently Honorary Research Fellow at the University of Glasgow, UK (2014-present). He was Associate Professor and Head of the Internet of Things (IoT) Group at the Athens In...