The past decade has seen explosive growth in development and training of artificial intelligence (AI) systems. However, as AI has taken on progressively more complex problems, the amount of computation required to train the largest AI systems has been increasing ten-fold annually. While AI advances are beginning to have a deep impact in digital computing processes, trade-offs between computational capability, resources and size, weight, and power consumption (SWaP) will become increasingly critical in the near future.
Current neuromorphic/neural architectures rely on the digital computing architectures that attempt to mimic the way nature computes, but not the way it functions. Actual physical interactions and mechanisms that could enable improved engineered function as observed in bio-systems, such as miniature insects, remain to be fully described.
μBRAIN will explore innovative basic research concepts aimed at understanding highly integrated sensory and nervous systems in miniature insects and developing prototype computational models that could be mapped onto suitable hardware to emulate their impressive function. Nature has forced on these small insects drastic miniaturization and energy efficiency, some having only a few hundred neurons in a compact form-factor, while maintaining basic functionality. This research could lead to capability of inference, prediction, generalization, and abstraction of problems in systematic or entirely news ways in order to find solutions to compelling problems.
The primary goal is to understand the computational principles, architecture, and neuronal details of small bio-systems driven by extreme SWaP needs in nature. By doing so, DARPA aims to identify new computing paradigms that would enable improved AI with considerably reduced training times and power consumption.
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