Technology companies specializing in compact, energy-efficient edge computing devices are actively working to enhance their devices’ capabilities to enable visual perception in machines for applications like facial recognition. Typically, visual perception demands significant computational resources when deployed remotely on edge devices, leading to reduced latency and ability to make real-time decisions.
Historically, the conventional use of deep learning and machine learning neural hardware, along with GPUs, has shown inefficiency in terms of energy consumption, especially when deployed near the network edge where data originates.
In response to this challenge, neuromorphic computing emerged as a solution, offering a form of artificial intelligence inspired by the human brain’s information processing methods. Using neuromorphic computing enables companies to create edge devices with a strong focus on energy efficiency, even while managing demanding AI workloads.
BrainChip partners with VVDN to develop Edge Box based on neuromorphic technology
BrainChip, a company known for its neuromorphic computing, has collaborated with VVDN Technologies, an electronics engineering and manufacturing firm, to create an Edge Box based on neuromorphic computing technology. This product is geared towards delivering advanced AI capabilities and finding applications in diverse domains such as security surveillance, automotive, and industrial use cases.
According to BrainChip, the Edge Box is designed to enable customers to deploy edge artificial intelligence applications in a cost-effective manner. Organizations can leverage the power of AI on edge devices for monitoring and security applications across various industries, offering a significantly more efficient and effective alternative to traditional approaches.
The Edge Box is a compact device with the capacity to execute AI models that support tasks like video analytics, facial recognition, and object detection. Leveraging the BrainChip Akida processor, known for its high performance, low power consumption, and scalable architecture, the Edge Box can be a suitable device for edge AI solutions.
“This portable and compact Edge box is a game-changer that enables customers to deploy AI applications cost-effectively with unprecedented speed and efficiency to proliferate the benefits of intelligent compute,” says Sean Hehir, chief executive officer at BrainChip.
