Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles

Most of today's computer vision pipelines are built around deep neural networks, where convolution operations require most of the generally high compute effort. The Winograd convolution algorithm computes convolutions with fewer multiply-accumulate operations (MACs) compared to the standard algorithm, reducing the operation count by a factor of 2.25× for 3×3 convolutions when using the version with 2×2sized tiles F 2 . Even though the gain is significant, the Winograd algorithm with larger tile sizes, i.e., F 4 , offers even more potential in improving throughput and energy efficiency, as it reduces the required MACs by 4×. Unfortunately, the Winograd algorithm with larger tile sizes introduces numerical issues that prevent its use on integer domain-specific accelerators (DSAs) and higher computational overhead to transform input and output data between spatial and Winograd domains.

To unlock the full potential of Winograd F 4 , we propose a novel tap-wise quantization method that overcomes the numerical issues of using larger tiles, enabling integer-only inference. Moreover, we present custom hardware units that process the Winograd transformations in a power-and area-efficient way, and we show how to integrate such custom modules in an industrial-grade, programmable DSA. An extensive experimental evaluation on a large set of state-of-the-art computer vision benchmarks reveals that the tap-wise quantization algorithm makes the quantized Winograd F 4 network almost as accurate as the FP32 baseline. The Winograd-enhanced DSA achieves up to 1.85× gain in energy efficiency and up to 1.83× end-toend speed-up for state-of-the-art segmentation and detection networks.