The main goal of this work is to improve the efficiency of training binary neural networks, which are low latency and low energy networks. The main contribution of this work is the proposal of two solutions comprised of topology changes and strategy training that allow the network to achieve near the state-of-the-art performance and efficient training. The time required for training and the memory required in the process are two factors that contribute to efficient training.

For this post, we measured fine tuning performance (training and inference) for the BERT (Bidirectional Encoder Representations from Transformers) implementation in TensorFlow using NVIDIA Quadro RTX 8000 GPUs. For testing, we used an Exxact Valence Workstation fitted with 4x Quadro RTX 8000's with NVLink, giving us 192 GB of GPU memory for our system. These tests measure performance for a popular use case for BERT and NLP in general, and are meant to show typical GPU performance for such a task. We made slight modifications to the training benchmark script to get the larger batch size metrics. The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, 8, 16, 32, and 64 for training, and 1, 2, 4, and 8 for inference.

The Raspberry Pi is one of the most exciting developments in hobbyist computing today. Across the world, people are using it to automate beer making, open up the world of robotics and revolutionise STEM education in a world overrun by film students. These are all laudable pursuits. Meanwhile, what is Microsoft doing with it? Over at the firm's Machine Learning and Optimization group, a researcher saw squirrels stealing flower bulbs and seeds from his bird feeder. The research team trained a computer vision model to detect squirrels, and then put it onto a Raspberry Pi 3 board.