HAWQV3: Dyadic Neural Network Quantization

Understand

Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values.

• This hidden cost limits the latency improvement realized by quantizing Neural Networks.
• To address this, we present HAWQV3, a novel mixed-precision integer-only quantization framework.
• The contributions of HAWQV3 are the following: (i) An integer-only inference where the entire computational graph is performed only with integer multiplication, addition, and bit shifting, without any floating point operations or even integer division; (ii) A novel hardware-aware mixed-precision quantization method where the bit-precision is calculated by solving an integer linear programming problem that balances the trade-off between model perturbation and other constraints, e.g., memory footprint and latency; (iii) Direct hardware deployment and open source contribution for 4-bit uniform/mixed-precision quantization in TVM, achieving an average speed up of $1.45\times$ for uniform 4-bit, as compared to uniform 8-bit for ResNet50 on T4 GPUs; and (iv) extensive evaluation of the proposed methods on ResNet18/50 and InceptionV3, for various model compression levels with/without mixed precision.