Embedded deep learning perception software designed for the automotive market
Brodmann17 creates highly efficient deep-learning products, while maintaining state-of-the-art quality. The neural-network models are based on two main technological breakthroughs that have been invented and perfected by our research team over the past few years.
Brodmann17's novel neural-network inference
The world’s fastest neural-network
Brodmann17 has engineered core deep-learning algorithms from the ground-up and patented a new neural-network technology, that is based on weights and calculation sharing. Brodmann17 deep neural networks (NN) leverage this technology to minimize the number of unique calculations within the NN so that a significant number of recurring calculations may be shared between neurons. The result is a NN with identical accuracy, however with a much smaller number of calculations, enabling faster and lower power-compute networks compared to the standard NN approach.
Standard single large pruned neural network
Brodmann17's patented solution
Weights & compute sharing neural-network
The Main Advantages
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The world’s first automated training platform:
designed for automotive-grade
Brodmann17 has developed a unique platform in order to swiftly bring automated driving to the mass market.
Brodmann17 developed this comprehensive platform that ensures the best neural-network for any given challenge and processing power by optimizing the entire neural-network training process based on our IP: the data selection, parameter tuning, neural network architecture search (NAS) and deployment to target embedded processors for runtime benchmarks.
scaling Brodmann17’s operations & improving complex processes
The training platform was developed as a neural network production line to scale Brodmann17’s operations and improve the highly complex process of training and NN deployment that cannot be done at scale manually.
Reducing time to market & costs
The training platform’s automation removes humans and lowers the associated risks of human error, while reducing time to market and costs.