Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference

• URL: http://arxiv.org/abs/2301.13330v2
• Date: Wed, 10 Jan 2024 19:04:59 GMT
• Title: Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
• Authors: Deepika Bablani, Jeffrey L. Mckinstry, Steven K. Esser, Rathinakumar Appuswamy, Dharmendra S. Modha
• Abstract summary: Quantizing networks to lower precision is a powerful technique for simplifying networks. Mixed precision quantization methods selectively tune the precision of individual layers to achieve a minimum drop in task performance. To estimate the impact of layer precision choice on task performance, two methods are introduced. Using EAGL and ALPS for layer precision selection, full-precision accuracy is recovered with a mix of 4-bit and 2-bit layers.
• Score: 3.3213055774512648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with the simplest networks requiring the least computation, memory, and power. Quantizing networks to lower precision is a powerful technique for simplifying networks. As each layer of a network may have different sensitivity to quantization, mixed precision quantization methods selectively tune the precision of individual layers to achieve a minimum drop in task performance (e.g., accuracy). To estimate the impact of layer precision choice on task performance, two methods are introduced: i) Entropy Approximation Guided Layer selection (EAGL) is fast and uses the entropy of the weight distribution, and ii) Accuracy-aware Layer Precision Selection (ALPS) is straightforward and relies on single epoch fine-tuning after layer precision reduction. Using EAGL and ALPS for layer precision selection, full-precision accuracy is recovered with a mix of 4-bit and 2-bit layers for ResNet-50, ResNet-101 and BERT-base transformer networks, demonstrating enhanced performance across the entire accuracy-throughput frontier. The techniques demonstrate better performance than existing techniques in several commensurate comparisons. Notably, this is accomplished with significantly lesser computational time required to reach a solution.

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