Deploying AI Models at the Edge

BrainChip, VVDN Technologies together develop Edge Box based on neuromorphic technology

BrainChip, VVDN Technologies together develop Edge Box based on neuromorphic technology

BrainChip, a company known for its neuromorphic computing devices, has formed a strategic partnership with VVDN Technologies, an electronics engineering and manufacturing firm. Together, they collaboratively developed the Edge Box, an edge device designed to process data and perform computations directly at the network edge.

The Edge Box hardware platform leverages neuromorphic technology, using AI algorithms and hardware architecture inspired by the complexities of human brain functionality. Both companies say the solution improves power efficiency and performance for applications situated at the edge.

“We are excited to bring the benefits of neuromorphic computing to the Edge AI market with VVDN as our lead partner,” says Sean Hehir, CEO of BrainChip. “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.”

The Edge Box is designed to serve a wide range of edge applications, including tasks such as video analytics, facial recognition, and object detection.

At the core of this solution lies BrainChip’s Akida processors, engineered to replicate the architecture and operation of the human brain. These processors exhibit the capability to tackle a broad spectrum of AI-related tasks while keeping power consumption at a minimum.

“The cost-effectiveness, efficiency and scalability of BrainChip’s Akida neuromorphic processor, coupled with VVDN’s solutions expertise should deliver a boost to the proliferation of customizable and secure AI applications at the Edge,” says Bram Geenen co-founder of Wevolver.

According to Marc Staimer, the president of Dragon Slayer Consulting, there is a growing market need for customized computing architectures that can match performance, power efficiency, cooling, portability, and cost criteria. He emphasizes the opportunity in the market for specialized edge AI solutions that can cater to the increasing demand for digital transformation.

BrainChip has recently released its 2023 Edge AI technology report, which discusses the current landscape of edge AI. This report investigates how platforms like TensorFlow Lite and Nvidia Jetson play an important role in supporting the deployment of edge AI, ensuring a secure and robust implementation of edge AI technologies.

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