EfficientQuant applies uniform weight quantization to CNN blocks and logarithmic activation quantization to transformer blocks in hybrid models, reporting latency reductions of 2.5x to 8.7x with modest accuracy loss.
HPTQ: Hardware-Friendly Post Training Quantization
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abstract
Neural network quantization enables the deployment of models on edge devices. An essential requirement for their hardware efficiency is that the quantizers are hardware-friendly: uniform, symmetric, and with power-of-two thresholds. To the best of our knowledge, current post-training quantization methods do not support all of these constraints simultaneously. In this work, we introduce a hardware-friendly post training quantization (HPTQ) framework, which addresses this problem by synergistically combining several known quantization methods. We perform a large-scale study on four tasks: classification, object detection, semantic segmentation and pose estimation over a wide variety of network architectures. Our extensive experiments show that competitive results can be obtained under hardware-friendly constraints.
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EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices
EfficientQuant applies uniform weight quantization to CNN blocks and logarithmic activation quantization to transformer blocks in hybrid models, reporting latency reductions of 2.5x to 8.7x with modest accuracy loss.