The exponential increase in generated data as well as the advances in high-performance
computing has paved the way for the use of complex machine learning methods. Indeed, the …

Over the past decades, emerging, data-driven machine learning (ML) paradigms have
increased in popularity, and revolutionized many application domains. To date, a substantial …

Self-attention-based transformer models have achieved tremendous success in the domain
of natural language processing. Despite their efficacy, accelerating the transformer is …

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with
past experiences, with fused memory and processing. In contrast, AI models are static …

Content-addressable memory (CAM) is a specialized type of memory that facilitates
massively parallel comparison of a search pattern against its entire content. State-of-the-art …

Transformer models have achieved impressive results in various AI scenarios, ranging from
vision to natural language processing. However, their computational complexity and their …

Content-Addressable Memory (CAM) circuits, distinguished by their ability to accelerate data
retrieval through a direct content-matching function, are increasingly crucial in the era of AI …

Transformer neural networks, driven by self-attention mechanisms, are core components of
foundational and Large Language Models. In generative transformers, self-attention uses …

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with
past experiences, with fused memory and processing. In contrast, AI models are static …

Large language models (LLMs) have recently transformed natural language processing,
enabling machines to generate human-like text and engage in meaningful conversations …