Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks
This work provides tools and an architecture for developers and researchers to efficiently deploy and design BNNs, which are crucial for resource-constrained edge devices.
This paper introduces Larq Compute Engine (LCE), an inference engine for Binarized Neural Networks (BNNs), which accelerates binary convolutions by 8.5 - 18.5x on Pixel 1 phones compared to full-precision. Using LCE, the authors developed QuickNet, a new BNN architecture that achieves state-of-the-art latency and accuracy on ImageNet.
We introduce Larq Compute Engine, the world's fastest Binarized Neural Network (BNN) inference engine, and use this framework to investigate several important questions about the efficiency of BNNs and to design a new state-of-the-art BNN architecture. LCE provides highly optimized implementations of binary operations and accelerates binary convolutions by 8.5 - 18.5x compared to their full-precision counterparts on Pixel 1 phones. LCE's integration with Larq and a sophisticated MLIR-based converter allow users to move smoothly from training to deployment. By extending TensorFlow and TensorFlow Lite, LCE supports models which combine binary and full-precision layers, and can be easily integrated into existing applications. Using LCE, we analyze the performance of existing BNN computer vision architectures and develop QuickNet, a simple, easy-to-reproduce BNN that outperforms existing binary networks in terms of latency and accuracy on ImageNet. Furthermore, we investigate the impact of full-precision shortcuts and the relationship between number of MACs and model latency. We are convinced that empirical performance should drive BNN architecture design and hope this work will facilitate others to design, benchmark and deploy binary models.