Integrating Neural Architectures for Brain-Inspired AI with Low SWAP

Description

This SBIR solicitation seeks innovative research and development of novel AI architectures that integrate multiple neural network paradigms into a unified framework. To develop novel hardware and software architectures that integrate multiple neural network modalities into a single system, inspired by the functional organization of the human neocortex. This research will explore innovative approaches to achieve low SWAP (Size, Weight, and Power) while enhancing computational efficiency and cognitive capabilities relevant to DoD applications.The objective of this research is to develop integrated neural architectures that:Combine multiple neural network models (e.g., convolutional neural networks, recurrent neural networks, attention-based mechanisms) into a single architecture.Emulate the emergent properties of cognitive processing in the neocortex, such as hierarchical organization and processing, contextual dependence and attention, memory and working memory, and decision-making and uncertainty estimation.Achieve low SWAP (Size, Weight, and Power) to enable deployment on resource-constrained platforms (e.g., wearable devices, drones, autonomous vehicles).Demonstrate improved performance and robustness over state-of-the-art architectures on various cognitive tasks (e.g., object recognition, natural language processing, spatial reasoning).