.Artificial Intelligence (AI) can power autonomous vehicles, provide strategic advantages through large-scale data analytics, and enable intelligence gathering and surveillance through advanced computer vision, opening a wide range of defence applications. Conventional Von Neumann architectures, despite their flexibility, are inefficient for AI workloads due to data duplication and data movement bottlenecks, which severely limit the efficiency in the rapid processing of large amount of information and streaming data needed by AI algorithms. In edge computing devices, crucial for defense applications, this inefficiency is compounded by energy limitations. To overcome these challenges, specialized hardware and programming paradigm shift are needed to accelerate AI workloads. With the end of Moore’s law and Dennard scaling, simply scaling up existing architectures is no longer viable. Novel architectures are needed to improve performance per Watt and bypass the efficiency limits imposed by the Von Neumann bottleneck. ARCHYTAS aims to investigate unconventional AI accelerators that take advantage of novel technologies: optoelectronic-based accelerators, volatile and non-volatile processing-in-memory, and neuromorphic devices. These technologies promise to mitigate the Von Neumann bottleneck by integrating processing and memory. ARCHYTAS also explores the integration of CMOS-based systems with analogue accelerators, as well as new programming models to improve the programmability, performance portability, and productivity of these emerging parallel systems through a hardware-AI co-design approach. The technological ambition of ARCHYTAS is to bridge the gaps in multi-modal sensing integration and AI processing, providing solutions that fit the non-functional requirements of future autonomous vehicles for defense applications. The ARCHYTAS AI accelerators will be validated within the context of defense AI use cases in land, aerial, maritime, and space settings.