Publication
ISCA 2023
Workshop

7th Workshop on Cognitive Architectures (CogArch 2023)

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Abstract

Artificial Intelligence (AI) and Machine Learning (ML) techniques have become the de facto solution to drive human progress and more specifically, automation. In the last years, the world’s economy has been gravitating towards the AI/ML domain (from industrial and scientific perspectives) and the expectation of growth is not withering away. In more recent years, a new trend towards very large AI models has been gaining traction: Foundation Models. Large Foundation Models with billions or trillions of parameters exhibit astonishing emergent skills, especially in fields like natural language processing, vision, and human-machine interaction. Examples like BERT (110M parameters), GPT-2 (1.5B parameters), GPT-3 (175B parameters), BLOOM (176B parameters), and Wu Dao (1.75T parameters!) indicate that the size of these models will keep growing rapidly and substantially in the foreseeable future. This results in all sorts of challenges, especially with respect to provisioning the computing resources needed for training and inferencing, and calls for thorough co-design across algorithms, models, software, and hardware systems. In this context, this edition of the CogArch workshop aims at bringing together the necessary know-how to address co-designed hardware-software architectures from a holistic point of view, tackling all their design considerations from the algorithms to platforms in all the different fields that large-scale cognitive systems will soon occupy, from natural language processing and speech to other applications like protein folding, drug discovery, computer vision or even music generation. The CogArch workshop already had six successful editions, bringing together experts and knowledge on the most novel design ideas for cognitive systems. This workshop capitalizes on the synergy between industrial and academic efforts in order to provide a better understanding of cognitive systems and key concepts of their design.