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Quantization is a technique for creating efficient Deep Neural Networks(DNNs), which involves performing computations and storing tensors at lowerbit-widths than f32 floating point precision. Quantization reduces model sizeand inference latency, and therefore allows for DNNs to be deployed onplatforms with constrained computational resources and real-time systems.However, quantization can lead to numerical instability caused by roundofferror which leads to inaccurate computations and therefore, a decrease inquantized model accuracy. Similarly to prior works, which have shown that bothbiases and activations are more sensitive to quantization and are best kept infull precision or quantized with higher bit-widths, we show that some weightsare more sensitive than others which should be reflected on their quantizationbit-width. To that end we propose MixQuant, a search algorithm that finds theoptimal custom quantization bit-width for each layer weight based on roundofferror and can be combined with any quantization method as a form ofpre-processing optimization. We show that combining MixQuant with BRECQ, astate-of-the-art quantization method, yields better quantized model accuracythan BRECQ alone. Additionally, we combine MixQuant with vanilla asymmetricquantization to show that MixQuant has the potential to optimize theperformance of any quantization technique.

MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search