BrainChip, Blue Ridge Envisioneering team up to build tactical edge devices

BrainChip, Blue Ridge Envisioneering team up to build tactical edge devices

BrainChip, a provider of neuromorphic processors for edge AI on-chip processing, has teamed up with Blue Ridge Envisioneering. Blue Ridge Envisioneering will integrate BrainChip Akida processors into Blue Ridge’s designs for tactical edge devices used by defense and intelligence agencies.

The co-developed tactical edge devices will deploy in harsh, resource-constrained environments with limited power access and strict thermal requirements. The BrainChip Akida platform’s proficiency in functioning effectively in extreme situations and its ability to offer advanced artificial intelligence capabilities at the edge makes it suitable for military applications.

“Almost all conventional Deep Neural Networks are developed to run on power-hungry GPUs that strain power systems and generate a significant amount of heat; when data must be processed in real-time on a remote device, the challenge for hardware developers is immense,” says Stefan Moritz, a software engineer at Blue Ridge Envisioneering.

Developers need to process real-time data on tactical devices with limited resources. This is a challenge since deep neural networks typically require graphics processing units that consume much power.

BrainChip’s Akida platform enables tactical devices to gather nearby data and execute advanced AI algorithms without requiring excessive power consumption. This application of Akida introduces the prospect of utilizing it in decision-making scenarios and in critical situations.

“The collaboration with BRE will show how Akida can excel where power and communications are in short supply. This joint effort will expand the boundaries of what AI at the edge can do in both tactical and commercial applications,” says Rob Telson, the vice president of ecosystem and partnerships at BrainChip.

BrainChip recently announced the development of its latest Akida platform, which is designed for embedded edge AI applications and features 8-bit processing. The new system utilizes vision transformers and Temporal Event Based Neural Nets (TENN) spatial-temporal convolutions to improve performance and power efficiency.

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