main webpage W Topic Artificial Intelligence arXiv (Cornell University) Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs 2024 Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix multiplications, leaving other layers like BatchNorm or shortcuts in floating-point… Article Open Artificial Intelligence Open Article Page

Artificial Intelligence

Intelligence of machines Artificial intelligence ( AI ) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g. Critical Symbolic Virtual Narrative Open Artificial Intelligence Open Article Page

Exploring foci of: arXiv (Cornell University) Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs 2024 Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix multiplications, leaving other layers like BatchNorm or shortcuts in floating-point form, even though fixed-point arithmetic is more efficient on FPGAs. A common practice is to fine-tune a pre-trained model to fixed-point for FPGA deployment, but potentially degrading accuracy. This work presents QFX, a novel trainable fixed-point quantizati… Open Article Page

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Explore Artificial Intelligence W Topic Artificial Intelligence arXiv (Cornell University) Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs 2024 Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix multiplications, leaving other layers like BatchNorm or shortcuts in floating-point… Article Critical Symbolic Virtual Narrative Open Artificial Intelligence Open Article Page