P Bit
"Bit" in the context of recent research encompasses diverse applications focusing on optimizing the efficiency and effectiveness of information representation and processing across various domains. Current research emphasizes minimizing bit usage in large language models (LLMs) and deep neural networks (DNNs) through techniques like quantization, coupled quantization, and novel binary representations, aiming to improve model compression, inference speed, and energy efficiency. These advancements have significant implications for deploying AI models on resource-constrained devices and enhancing the scalability of machine learning applications, while also addressing challenges in multilingual data processing and data privacy.
Papers
All-to-all reconfigurability with sparse and higher-order Ising machines
Srijan Nikhar, Sidharth Kannan, Navid Anjum Aadit, Shuvro Chowdhury, Kerem Y. Camsari
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
Shivam Aggarwal, Hans Jakob Damsgaard, Alessandro Pappalardo, Giuseppe Franco, Thomas B. Preußer, Michaela Blott, Tulika Mitra