The most ambitious algorithmic acceleration technique for aggressive speedups is neural architecture search (NAS). To use NAS one should define an architecture space and use a clever search strategy to search this space for an architecture that satisfies the desired properties. NAS optimizations are responsible for monumental achievements in deep learning. For instance, MobileNet-V3 and EfficientNet(Det) were found using NAS.

NAS algorithms typically require huge computational resources and therefore, applying them in a scalable manner in production is extremely challenging and expensive. Deci’s, AutoNAC brings into play a new, fast and compute efficient generation of NAS algorithms allowing it to operate cost effectively and at scale. The AutoNAC engine is hardware and data aware and considers all the components in the inference stack, including compilers and quantization.

Deci’s enables customers to build custom models that are 2-5x faster and more efficient allowing for: