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While neural networks have advanced the frontiers in many machine learningapplications, they often come at a high computational cost. Reducing the powerand latency of neural network inference is vital to integrating modern networksinto edge devices with strict power and compute requirements. Neural networkquantization is one of the most effective ways of achieving these savings, butthe additional noise it induces can lead to accuracy degradation. In this whitepaper, we present an overview of neural network quantization using AI ModelEfficiency Toolkit (AIMET). AIMET is a library of state-of-the-art quantizationand compression algorithms designed to ease the effort required for modeloptimization and thus drive the broader AI ecosystem towards low latency andenergy-efficient inference. AIMET provides users with the ability to simulateas well as optimize PyTorch and TensorFlow models. Specifically forquantization, AIMET includes various post-training quantization (PTQ, cf.chapter 4) and quantization-aware training (QAT, cf. chapter 5) techniques thatguarantee near floating-point accuracy for 8-bit fixed-point inference. Weprovide a practical guide to quantization via AIMET by covering PTQ and QATworkflows, code examples and practical tips that enable users to efficientlyand effectively quantize models using AIMET and reap the benefits of low-bitinteger inference.

Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)