main webpage W Topic Algorithm 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 Algorithm Open Article Page

Algorithm

Sequence of operations for a task In mathematics and computer science, an algorithm ( ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a heuristic is an approach to solving problems without well- defined correct or optimal results. For example, although social media recommender systems are commonly called "algorithms", they actually rely on heuristics as there is no truly "correct" recommendation. Critical Symbolic Virtual Narrative Open Algorithm 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

Click Algorithm Vs: Quantization (Signal Processing) Field-Programmable Gate Array Acceleration Computer Science Deep Learning Artificial Intelligence Mathematics Physics Embedded System Open Article Page

Click Algorithm Vs: Geometry Classical Mechanics Open Article Page

Explore Algorithm W Topic Algorithm 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 Algorithm Open Article Page