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Showing 1 to 3 of 3 for “"Quantization-aware training"”.
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Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization
We consider the problem of accurate quantization for language models, where both the weights and activations are quantized to 4 bits per parameter with uniform quantization, the lowest bitwidth format natively supported by existing GPU hardware. In this context, the key challenge is activation …
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Energy-efficient Neuromorphic Computing for Resource-constrained Internet of Things Devices
… four essential methods. The first method is the quantization of neural networks through knowledge distillation. This work introduces a quantization technique that effectively reduces the computational and storage resource requirements of a model while minimizing the loss of accuracy. To further …
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Comparing the Performance of Small Word-Size Floating-Point Numerics to Fixed-Point Numerics in Neural Networks
… architectures grow in depth and complexity, training them efficiently under hardware constraints has become increasingly important. While fixed-point arithmetic offers resource advantages, it suffers from limited dynamic range and quantization inflexibility. This thesis introduces an …