Enhancing Deep Neural Network Training with 1-Bit Fully Quantized Training (FQT)
Revolutionizing AI Training for Practical Solutions and Value
Deep neural network training can be accelerated through Fully Quantized Training (FQT) which reduces precision for quicker calculation and lower memory usage. FQT minimizes numerical precision while maintaining training effectiveness, with researchers exploring 1-bit FQT viability.
Researchers have introduced practical solutions, such as Activation Gradient Pruning (AGP) and Sample Channel joint Quantisation (SCQ), to improve training efficiency and accuracy. Their methodology has shown significant accuracy gains and a 5.13 times faster training process compared to full-precision training.
This study advances fully quantized training, lowering the acceptable threshold for numerical precision without compromising performance. It paves the way for more effective neural network training techniques, especially with the increasing use of low-bitwidth hardware.
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This AI Research from China Introduces 1-Bit FQT: Enhancing the Capabilities of Fully Quantized Training (FQT) to 1-bit
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