main webpage W Topic Mathematics 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 Mathematics Open Article Page

Mathematics

Area of knowledge Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics with the major subdisciplines of number theory, algebra, geometry, and analysis, respectively. There is no general consensus among mathematicians about a common definition for their academic discipline. Most mathematical activity involves the discovery of properties of abstract objects and the use of pure reason to prove them. These objects consist of either abstractions from nature or—in modern mathematics—entities that are stipulated to have certain properties, called axioms. Critical Symbolic Virtual Narrative Open Mathematics 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 Mathematics W Topic Mathematics 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 Mathematics Open Article Page